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LETGAMES: An LLM-Powered Gamified Approach to Cognitive Training for Patients with Cognitive Impairment
Jingwei Shi, Shengyu Tao, Xinxiang Yin, Chen Huang, Wenqiang Lei, See-Kiong Ng
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Summary
The paper introduces LETGAMES, an LLM-powered framework for automated, personalized, and safe cognitive training games for patients with cognitive impairment. It utilizes a dual-track multi-agent architecture comprising a Game Master (GM) for therapeutic content generation and a Psychology Master (PM) for emotional safety. The system is validated using LETGAMESEVAL, a psychology-grounded evaluation protocol, demonstrating superior therapeutic personalization and safety compared to existing serious games.
Entities (16)
Relation Signals (15)
LETGAMES â evaluatedby â LETTGAMESEVAL
confidence 95% ¡ we pioneer a psychology-grounded evaluation protocol LETGAMESEVAL... To validate LETGAMESâs efficacy
LETGAMES â targets â Cognitive Impairment
confidence 95% ¡ therapeutic game design... for patients with cognitive impairment
LETGAMES â uses â LLM
confidence 95% ¡ LETGAMES, a Large Language Model (LLM) driven method
LETGAMES â comprises â Game Master
confidence 92% ¡ dual-track multi-agent architecture... Game Master (GM)... Psychology Master (PM)
LETGAMES â comprises â Psychology Master
confidence 92% ¡ dual-track multi-agent architecture... Game Master (GM)... Psychology Master (PM)
Psychology Master â contains â Hint Provider
confidence 90% ¡ It deploys a Hint Provider (A HP )
Game Master â contains â Game Designer
confidence 90% ¡ The Game Designer (A GD ) constructs the environment
Game Master â contains â Game Controller
confidence 90% ¡ the Game Controller (A GC ) supervises the runtime process
Game Master â â
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Abstract
Abstract:The application of games as a therapeutic tool for cognitive training is beneficial for patients with cognitive impairments. However, effective game design for individual patient is resource-intensive. To this end, we propose an LLM-powered method, \ours, for automated and personalized therapeutic game design. Inspired by the Dungeons & Dragons, LETGAMES generates an open-world interactive narrative game. It not only generates game scenarios and challenges that target specific cognitive domains, but also employs conversational strategies to offer guidance and companionship. To validate its efficacy, we pioneer a psychology-grounded evaluation protocol LETGAMESEVAL, establishing comprehensive metrics for rehabilitative assessment. Building upon this, our experimental results from both LLM-based assessors and human expert evaluations demonstrate the significant potential of our approach, positioning LETGAMES as a promising solution to the widespread need for more accessible and tailored cognitive training tools. Our code will be open-sourced upon acceptance.
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- Source: https://arxiv.org/abs/2604.09566v1
- Canonical: https://arxiv.org/abs/2604.09566v1
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LETGAMES: An LLM-Powered Gamified Approach to Cognitive Training for Patients with Cognitive Impairment Jingwei Shi 1 Shengyu Tao 1 Xinxiang Yin 2 Chen Huang 3 * Wenqiang Lei 4 See-Kiong Ng 3 1 Shanghai University of Finance and Economics, China 2 Northwest Polytechnical University Xiâan, China 3 Institute of Data Science, National University of Singapore, Singapore 4 College of Computer Science, Sichuan University, China shijingwei@stu.sufe.edu.cn huang_chen@nus.edu.sg Abstract The application of games as a therapeutic tool for cognitive training is beneficial for patients with cognitive impairments. However, effec- tive game design for individual patients is resource-intensive. To this end, we propose an LLM-powered method, LETGAMES, for au- tomated and personalized therapeutic game de- sign. Inspired by the Dungeons & Dragons, LETGAMES generates an open-world interac- tive narrative game. It not only generates game scenarios and challenges that target specific cognitive domains, but also employs conversa- tional strategies to offer guidance and compan- ionship. To validate its efficacy, we pioneer a psychology-grounded evaluation protocol LETGAMESEVAL, establishing comprehensive metrics for rehabilitative assessment. Building upon this, our experimental results from both LLM-based assessors and human expert eval- uations demonstrate the significant potential of our approach, positioning LETGAMES as a promising solution to the widespread need for more accessible and tailored cognitive train- ing tools. Our code is available athttps: //github.com/shi0712/LETGAMES. 1 Introduction Fueled by an aging population and the rising prevalence of neurodegenerative diseases like Alzheimerâs, the global burden of cognitive im- pairment 1 is escalating (Khalil et al., 2025; MorĂĄn et al., 2024; Subramaniam et al., 2025), with pa- tient numbers projected to surpass 150 million by 2030 (Livingston et al., 2020). This motivates the demand for cognitive health solutions, especially non-pharmacological interventions like cognitive training (Choi et al., 2025). Serious Games (SGs), games designed for a primary purpose beyond en- tertainment (Charlier et al., 2016; Manera et al., 2017), have emerged as a particularly promising * Corresponding author. 1 Appendix A offers background on cognitive impairment. Game for Attention Training Game for Verbal learning TODOs: -Personalization âTailored to patient profile ď°Tailored to one cognitive domain -Safety ď°Give game guidance ď°No riskcontent ď°... Game Design for Cognitive Impairment is Labor-intensive Domain-Specific Cognitive Training Games MemoryAttentionVerbalExecutive Func.Social Game Expert Clinical Therapist Figure 1: Creating an effective game for cognitive train- ing demands a joint effort between experts, making the development process labor-intensive. tool (DellâOsso et al., 2024; Anguera and Gazza- ley, 2015; Tan et al., 2023). Unlike other cognitive training methods like Transcranial Magnetic Stim- ulation (Nardone et al., 2014), SGs offer a scalable, affordable, and highly accessible therapeutic solu- tion, alleviating the burden on centralized hospital facilities (Ning et al., 2020). Consequently, various SGs have been developed, ranging from traditional board games (Dartigues et al., 2013) to immersive virtual reality experiences (Padala et al., 2012), tar- geting general cognition (Bing, 2014) or specific impairments (Benveniste et al., 2012; Manera et al., 2015; CatricalĂ et al., 2025). However, creating an effective cognitive train- ing game demands a joint effort between game experts and clinical therapists, which is labor- intensive (Yang et al., 2024; Canapa et al., 2025; Wang et al., 2024b). This complexity stems from two critical requirements:1)Personalization. As individuals experience deficits in specific cogni- tive domains (Bowie and Harvey, 2005; Kahn and Keefe, 2013), games must be tailored to target distinct faculties, such as attention (Tong et al., arXiv:2604.09566v1 [cs.HC] 18 Feb 2026 Serious Games Cognitive Domain Design Cost Extra Safety Protection Open Sourced Brief Intro. MINWii (Benveniste et al., 2012) MemoryHigh (Human-generated)NoNoMusic therapy game Kitchen and Cooking (Manera et al., 2015) Attention, Executive Function High (Human-generated)Game guidanceNoRecipe-based cooking game Big Brain Academy (Dinis et al., 2019) Memory, Executive Function High (Human-generated)Game guidanceNoPuzzle video game ReMe (Wang et al., 2024b) Memory Medium (LLM-expert co-generated) Game guidanceNo Conversational game without narratives; Two human-designed games LETGAMES (ours) Memory, Attention, Verbal Learning, Executive Function, Social Cognition Low (LLM-generated) Game guidance, Conversational companionship YesConversational narrative game Table 1: Representative serious games for cognitive training. Such games are not only costly to create but also limited in scope, failing to cover varying cognitive domains or provide adequate safety assurances. 2017), verbal learning (Wong et al., 2019), mem- ory (Chang et al., 2013), and executive function (LĂłpez-MartĂnez et al., 2011; Manera et al., 2015), while adapting to individual user profiles.2)Safety. Patients with cognitive impairment frequently suf- fer from comorbid depressive symptoms (Modrego and FerrĂĄndez, 2004). Additionally, they are sus- ceptible to frustration arising from in-game failure, which hinders therapeutic progress (Cramer et al., 2009; Choi et al., 2025). Given their psychologi- cal vulnerability, mitigating negative emotions and ensuring game safety is critical (Choi et al., 2025). Therefore, these dual requirements render the man- ual design of SGs prohibitively expensive. To this end, we introduce LETGAMES, a Large Language Model (LLM) driven method de- signed to automate the creation of personaLized, safETherapeuticGAMES. Inspired by Dun- geons & Dragons (Wikipedia, 2025-11-05), LETGAMES generates an open-world interactive narrative game 2 , which ensures flexibility in game content and accessibility for cognitively impaired users. Basically, LETGAMES relies on a dual-track multi-agent architecture to ensure therapeutic effi- cacy: A coalition of agents functions as the Game Master (GM), facilitating therapeutic personaliza- tion by generating game scenarios and challenges tailored to pre-defined cognitive domains and in- dividual user profiles. Simultaneously, a separate agent cluster operates as the Psychology Master (PM) to guarantee psychological safety. It em- ploys conversational strategies to offer game guid- ance and emotional companionship, effectively mit- igating adverse reactions to negative emotion. As such, LETGAMES presents a scalable solution to the widespread need for tailored cognitive training. 2 Appendix E details multimodal extensions of SGs. To validate LETGAMESâs efficacy, we pio- neer a psychology-grounded evaluation protocol, LETGAMESEVAL, tailored for SGs targeting cog- nitive impairment. Unlike conventional evaluations that rely on resource-intensive and ethically com- plex longitudinal clinical trials, we adopt therapeu- tic interaction analysis as a scalable, low-risk proxy for treatment quality (Wang et al., 2025; Ning et al., 2020; Sobowale and Humphrey, 2025). Building on this, we propose a holistic framework designed for efficient rehabilitative assessment, filling a crit- ical evaluation gap within the SG domain (Ning et al., 2020). LETGAMESEVAL establishes a com- prehensive metric suite covering therapeutic qual- ity, safety, game content, and user engagement, enabling an effective, multi-dimensional evalua- tion without the immediate constraints of high-risk clinical deployment. With LETGAMESEVAL, we conduct experi- ments to assess the cognitive training effective- ness of LETGAMES. Due to ethical considerations, we adopt the common practice in psychotherapy of using SPs to ensure controlled and repeatable evaluations (Barrows, 1993; Nestel and Bearman, 2014; MacLean et al., 2017; Elendu et al., 2024). Specifically, we employed both human-portrayed and LLM-based SPs (Na et al., 2025; Lee et al., 2025) to interact with LETGAMES. Empirical re- sults indicate that LETGAMES exhibits superiority over existing baselines in terms of therapeutic per- sonalization and psychological safety. To sum up, our main contributions are as follows. â˘We tackle the labor-intensive process of manu- ally designing serious games for patients with cognitive impairment. ⢠For the first time, we propose an LLM-powered method, LETGAMES, for the automated design of SGs for cognitive training. It utilizes a multi- agent architecture to ensure the therapeutic per- sonalization and psychological safety. â˘We pioneer a psychology-grounded evaluation protocol LETGAMESEVAL, establishing compre- hensive metrics for multi-dimensional rehabilita- tive assessment. â˘We experimentally demonstrate the significant potential of LETGAMES as an effective, accessi- ble, and tailored cognitive training tools. 2 Related Work SGs for Cognitive Impairment Training. Beyond their application in cognitive assessment (Jimison et al., 2004; Yang et al., 2024), SGs are also well- established tools for cognitive training (Ning et al., 2020; Canapa et al., 2025), driven by mounting evidence of their effectiveness in treating cognitive impairment, particularly in frontal and executive function (Vogel et al., 2006; MondĂŠjar et al., 2016). Notable examples of such games include MINWii (Benveniste et al., 2012), Kitchen and Cooking (Manera et al., 2015), and Big Brain Academy (Di- nis et al., 2019; Wikipedia, 2025-10-24a). While the recently developed ReMe (Wang et al., 2024b) utilizes LLMs for content generation, it still neces- sitates human expertise for designing game logic. As summarized in Table 1, these methods typi- cally entail manual, resource-intensive processes involving psychologists and game designers. These constraints make production costly and slow, of- ten preventing the coverage of diverse cognitive domains or the lack of adequate safety guarantees. LLM for Psychotherapy. The integration of LLMs has marked a significant evolution in psy- chotherapy (Na et al., 2025), with applications spanning clinical assessment (Jimison et al., 2004; Yang et al., 2024), diagnosis (Cao et al., 2025; Chen et al., 2023b), treatment (Xiao et al., 2024; Sharma et al., 2023), data generation (Lee et al., 2024; Du et al., 2025), and the evaluation of therapeu- tic systems (Zhang et al., 2024; Wang et al., 2024a). To overcome ethical barriers, many studies em- ploy LLM-driven SPs for psychological assessment (Elendu et al., 2024; Almansoori et al., 2025; Wang et al., 2024a). They also employ LLMs to analyze therapy logs (e.g., patient-therapist interactions and conversations) and assess the quality of therapeu- tic treatment (Ning et al., 2020; Wang et al., 2025; Sobowale and Humphrey, 2025; Fitzpatrick et al., 2017). In this work, we propose LETGAMES, a gamified method that leverages LLMs to deliver fully autonomous, personalized, and safe cognitive training. Evaluation for Cognitive Training. While con- ventional evaluation measures changes in the cogni- tive abilities of patients undergoing treatment (Yu and Chan, 2021; Choi et al., 2025; Wang et al., 2024b), such methods are resource-intensive and fraught with ethical risks. Emerging research sug- gests analyzing therapeutic interactions as a viable, low-risk proxy for treatment quality (Wang et al., 2025; Ning et al., 2020; Sobowale and Humphrey, 2025) to bridge the gap between clinical rigor and practical feasibility. Furthermore, existing evalua- tions of SGs for cognitive impairment rarely con- sider critical dimensions such as game safety(Wang et al., 2024b), resulting in a fundamentally incom- plete assessment landscape. Given the lack of stan- dardized assessment in the field (Ning et al., 2020), LETGAMESEVAL provides a holistic metric suite, enabling rigorous evaluation. 3LETGAMES Overview. As illustrated in Figure 2, LETGAMES generates open-world interactive narratives via a dual-track multi-agent architecture. Formally, we define the training session as a tupleT = â¨D,P,G, ΨâŠ, whereDrepresents the target cog- nitive domain,Pdenotes the patient profile (e.g., age and life experience),Gis the generated game environment, andΨrepresents the psychological safety mechanisms. The system operates through two distinct functional units: the Game Mas- ter (GM), responsible for maximizing therapeu- tic efficacy viaG, and the Psychology Master (PM), responsible for optimizing safetyΨ. Specif- ically, our game process initializes when a ther- apist specifies a target domaind â Dand up- loads a profilep â P. The GM initiates a col- laborative multi-agent workflow to dynamically synthesize scenarios and challenges tailored to these requirements: TheGame Designer(A GD ) constructs the environment, defining the game logic and initial scenario, theGame Controller(A GC ) supervises the runtime process, generating imme- diate gameplay challenges while ensuring narra- tive coherence and dynamic state management, and theGame Critic(A GCr ) evaluates the game content (e.g., checking language complexity) and incorporates reflection mechanisms (Shinn et al., 2023) to provide iterative feedback to the Con- troller. Simultaneously, the PM also deploys an agent team. It deploys aHint Provider(A HP ) is prompted to proactively offer essential guidance, while anEmotion Copilot(A EC ) fosters compan- ionship and delivers emotional regulation via tai- lored dialog strategies. Finally, operating in the background, LETGAMES archives the gameplay history, enabling clinical therapists to track patient progress longitudinally when necessary. 3.1 Game Master (GM) A coalition of LLM agents functions as the GM, facilitating therapeutic efficacy by generating per- sonalized game scenarios and challenges. Given a target cognitive domaindand a patient profilep (e.g., age and life experience), the GM constructs a gamegfeaturing coherent narratives that align with clinical goals. To ensure the content is both en- gaging and clinically appropriate, the GM employs three agents that collaborate through an iterative refinement process driven by agent feedback. Game Designer (A GD ).A GD is responsible for generating the architect of the therapeutic game specificationG 0 and initial challenges as follows. These information offers necessary guidance to the subsequent operations of the game controller. G 0 = (G scenario ,G task ) = A GD (d,p).(1) â˘GameScenario(G scenario ). Grounded in clinical evidence that Activities of Daily Living (ADL)- based training strengthens frontal function, mak- ing it an effective way for cognitive training (Mlinac and Feng, 2016; Saunders and Summers, 2011), LETGAMES uses ADL as a validated paradigm for cognitive training. To operational- ize this principle, theA GD agent constructs a tailored ADL scenario that is contextualized by the patientâs background and profile. Taking Fig- ure 2 (right) for example, LETGAMES designs a scenario for a retired, middle-aged individual cen- tered on a social gathering at a community center. To ensure a structured and controllable generation process,A GD operates on a slot-filling paradigm (Cao et al., 2025; Das et al., 2024). It leverages a predefined game element schema 3 , including information on the scenario and non-player char- acter (NPC), to guide the LLM in systematically populating each component. â˘GameTask(G task ). Given a target cognitive do- maindand a patient profilep, theA GD agent is 3 Detailed in Table 6 and Table 7 in Appendix. tasked with generating an operationally clear and therapeutically relevant cognitive training task. For training memory and verbal learning, we adopt the clinically established cognitive process (Curtiss et al., 2001; Martin et al., 1985), decom- posing the task into three distinct subtasks via Chain-of-Thought (CoT). This involves: (1) an encoding subtask, where the user learns key infor- mation; (2) a retention subtask, featuring several distractor tasks like NPC conversations to chal- lenge memory consolidation; and (3) a retrieval subtask, where the user must recall the informa- tion to solve a new problem. For other domains, we prompt the LLM to directly generate tasks that target the core underlying abilities. To opera- tionalize these tasks,A GD also initializes game challenges by framing the tasks at hand into ques- tions (e.g., for the memory encoding subtask, it might ask, can you remember these three names and repeat). Finally, a Cognitive Tracker evalu- ates the userâs cognitive state via an LLM-based scoring. This provides a clinical reference for staff and allowsA GD to dynamically calibrate game difficulty based on the resulting scores (see Section 5.3). Game Controller (A GC ). Acting as the runtime engine,A GC is responsible for ensuring narra- tive coherence and dynamic state management. The stateSis a tuple comprising four compo- nents:(S task ,S scenario ,S user ,S conv ), where the task stateS task includes the game progress descrip- tion,S scenario includes the set of active NPCs, visi- ble items, and the scene description,S user contains the userâs inventory, location, and situational con- text, andS conv is the conversation history/state. At timet,A GC continuously monitors the game state S t and user responsea t to synthesize the immediate next steps of gameplay, with the aim of maintaining narrative continuity. S t+1 = A GC (S t ,a t ,H t ).(2) To achieve this,A GC maintains a memory module Mthat records the patientâs past responses, en- suring that future narrative turns and challenges remain consistent with historical actions. Beyond reactive updates,A GC also follows the designerâs task specifications to generate contextual chal- lenges. For example, after a user selects an item in the supermarket game,A GC generates a logically consequent challenge, such as âExcellent. Now, can Game Master Game Designer í¨ íŽíŤ Create & Design Game Controller í¨ íŽíŞ Manage Gameplay Game Critic í¨ íŽíŞí Evaluate & Feedback Overview of LETGAMES Scene Background: Imagine you are in a community center. Its warm rays dancing in every corner ... Task Goal: Please reflect on your teaching career and share three memorable stories with us. LETGAMES: You and Dr. Zhang are in the activity room. The mood is light and pleasant, with photos of community activities on the walls. What would you do next? (Hint:review the script for story details) You: I pick up the script LETGAMES: ...(game continue) LLM-powered SG Name: X; Age: 47; Gender: Female; Occupation: Teacher Life Experience: ... .... Profile Cognitive Domain to Train: Memory Input Info. Gameplay Archive Gameplay Record (include LLM-scoring for cognitive state) Enable clinical staffs to track patient progress & conduct future analysis Psychology Master Hint Provider í¨ íŻíˇ Give Guidance Emotion Copilot í¨ íŹíŞ Support & Care Game Scenario Generation Profile Game Task Generation Encoding Subtask Retention Subtask Retrieval Subtask Game State Modeling S íĄ Task State S íĄíí í Memory M State S íĄ+1 & Response Game Quality Check Narrative Coherence Factual Accuracy Clinical Boundaries LLM Reflection Scenario State S íííííííí User State S í˘í íí Conversation State S ííííŁ Emotional State í íĄ Track State í íĄâ1 , Game Performance P íĄ , Behavior íľ íĄ , Contextual Info. íś íĄ Current í íĄ & Appease Conv. Strategy Game Response Hint Provision Strategies Current Game State - Gentle Reminders - Concrete Structural Hints - Direct Assistance Hint Strategy Refine Review Update profile Figure 2: Overview of LETGAMES. It utilizes a dual-track multi-agent architecture comprising a Game Master and a Psychology Master to ensure flexibility in game content and accessibility for cognitively impaired users. you recall if there was anything else on your shop- ping list?â, to sustain immersion. Once the success conditions are met (e.g., all required items have been purchased),A GC ends the game. Crucially, to safeguard the userâs emotional well-being, we requireA GC to respond in therapeutic tone with warmth, patience, and encouragement. Game Critic (A GCr ). Acting as an evaluator, the critic ensures high standards of game quality and forms an iterative refinement loop along with the A GC . Specifically, it validates the content against strict criteria, including narrative coherence, factual accuracy, and clinical boundaries. It audits the con- tent for cultural and age appropriateness, ensuring that language complexity is attuned to the userâs cognitive level. Following Madaan et al. (2023); Chen et al. (2024), if any violations are identified, the critic issues a revision request toA GC , instruct- ing it to regenerate the problematic segments based on specific corrective feedback. 3.2 Psychology Master (PM) While the game master drives the narrative, the PM prioritizes the patientâs emotional safety. After each user response, the PM is triggered to perform a assessment of the patientâs game state and emotion condition. This is realized through the coordinated efforts of two empathetic agents. Hint Provider (A HP ). To prevent frustration dur- ing cognitive challenges (Cramer et al., 2009), A HP provider implements hierarchical scaffold- ing instructions. This proactively provides contin- uous guidance to ensure they eventually complete the task successfully. Specifically,A HP generates hints at three distinct levels of granularity based on the userâs error rate and fatigue, where fatigue is measured via LLM-as-judge based on game state S. 1) Gentle reminders or directional cues (e.g., "Have you checked all items on the list?"). 2) Con- crete structural hints (e.g., "It seems you forgot to pick up the eggs"). 3) Direct assistance to prevent task failure and disengagement (e.g., "Letâs head to the dairy aisle to get two eggs."). To govern when and how these hints are delivered, we utilize mixed- initiative prompts (Deng et al., 2023; Chen et al., 2023a), which encode goal-oriented strategies into the prompt to guide the modelâs output (Appendix B.1.2). This obviates the need to train a dedicated model on sensitive clinical data, and ensures the guidance is dynamically tailored to the patientâs needs. Emotion Copilot (A EC ).A EC serves as a real- time emotional guardrail, tasked with detecting and mitigating instances of patient emotional distress. Formally, at each time stept,A EC maps a feature vectorX t and the previous emotional statee tâ1 to the current emotional state 4 e t (e.g., calm, excited, confused) e t = A EC (X t ,e tâ1 ),(3) where the feature vectorX t is a concatenation of multi-modal data, including game performance metricsP t (i.e., success rate, hint usage count), behavioral featuresB t (i.e., patient response la- tency), and contextual informationC t (i.e., game duration, time since last break). Upon detecting distress,A EC intervenes not with game mechanics, but with conversations to stabilize their psychologi- cal state before resuming cognitive training, such as 4 Detailed in Table 5 in Appendix. guiding the user through deep breathing exercises and attributional reframing (Haynes et al., 2009; Haynes Stewart et al., 2011), where the difficulty is externalized (e.g., "this task is designed to be quite challenging"). To achieve this, we follow Cao et al. (2025) and equipA EC with specified conversation strategies 5 and utilize mixed-initiative prompts (Deng et al., 2023; Chen et al., 2023a). Furthermore, if a user experiences repeated fail- ure,A EC initiates a reset strategy, transitioning the patient to an easier game. 4LETGAMESEVAL Due to ethical considerations, we follow cur- rent common practice of assessing the therapeutic treatment (Ning et al., 2020; Wang et al., 2025; Sobowale and Humphrey, 2025; Fitzpatrick et al., 2017; Wang et al., 2024b; Jiang et al., 2023), and in- struct evaluators to analyze gameplay records (con- versations) and assess the therapeutic quality of the training process. This leads to our psychology- grounded evaluation protocol. 4.1 Evaluation Protocol Overview. Patients with specific cognitive impair- ments are instructed to interact with games gener- ated by different methods. Adhering to established practices in rehabilitation evaluation (Ning et al., 2020; Wang et al., 2025), LETGAMESEVAL eval- uates efficacy based on the analysis of gameplay records using metrics detailed in next subsection. Finally, to ensure robustness, LETGAMESEVAL reports the average performance for each method, aggregated across all participating patients. Patients & Game-play Simulation. Due to ethi- cal considerations, we adopt the common practice in psychotherapy of usingStandardizedPatients (SPs)to ensure controlled and repeatable evalua- tions (Barrows, 1993; Nestel and Bearman, 2014; MacLean et al., 2017; Elendu et al., 2024). Specif- ically, LETGAMESEVAL employs both human- portrayed (Varrecchia et al., 2020; Akkurt Yalcin- turk and Dissiz, 2024; Cotter et al., 2025) and LLM- based SPs 6 (Na et al., 2025; Lee et al., 2025) to generate gameplay records. Crucially, LETGAME- SEVAL introduceshealthycontrolsas a reference group to play the game alongside SPs for rigorous analysis. Since evaluators analyzing the gameplay records are blinded to the source (SP vs. Control), 5 Detailed in Appendix B.1.2. 6 Refer to Appendix C.1.5 for reliability analysis of SPs. healthy controls serve as a tool to isolate game- level failures from patient-specific deficits. 4.2 Evaluation Metrics LETGAMESEVAL comprises a four-fold metrics to guide evaluators, detailed in Appendix C.2.1. Higher scores denote superior performance. Therapeutic Training Quality. We utilize the framework ofTreatmentFidelity(Moncher and Prinz, 1991; Clare et al., 2019), which measures adherence to the intended protocol, and verifies that treatment conditions differ as intended: ⢠Helpfulness(Help. â [0, 5]). Evaluators assess how effectively the game exercises cognitive abil- ities based on game-play records. We report the average score on the evaluation dataset. ⢠Domain Alignment(DoAl. â [0, 100%]). It checks if the game aligns with target training do- main. Evaluators analyze each gameplay record and annotate the set of perceived cognitive do- mains. We compare these annotated domains against the ground truth to calculate the F1 score. Game Safety. Games must mitigate potential ad- verse effects, given patientsâ vulnerability. ⢠Safety(Safe. â [0, 100%]) measures if game outputs contain toxic content. Evaluators flag game-play records as safe or unsafe based on the established risk behaviors 7 , with the final score reporting the percentage of safe logs. ⢠Necessary Hints(NeHi. â [0, 100%]) deter- mines if the game provides timely guidance at critical junctures to prevent user frustration. It is defined as the ratio of provided hints to the total number of instances where guidance is required. ⢠Anxiety-free(Anxi. â [0, 100%]). We instruct evaluators to track user emotional states and as- sess the prevalence of anxiety-free interactions, reporting the average frequency of sessions de- void of intense negative emotions. ⢠Alleviation(Alle.â [0, 100%]). It measures the success rate in mitigating detected anxiety. Game Content & User Engagement. They are critical for ensuring long-term patient adherence and treatment acceptance (Choi et al., 2025). ⢠Easy-to-Use (Easy. â [0, 5]). We assess game difficulty by referencing the NASA-TLX scale (Hart and Staveland, 1988). 7 cf. Table 9 in Appendix C.2.1. Game UserMethods Training QualityGame SafetyGame Content & User Engagement Help.DoAl.Safe.NeHi.Anxi.Alle.Easy.Cohe.Pers.Enjo.Will. Game Backbone: GPT-4o Mini LLM-based SPs LETGAMES4.8587.20%99.83%100.00%98.36%94.18%3.604.144.533.834.06 ReMe3.51â100.00%45.78%99.13%â3.40â2.472.45 LLM-based Controls LETGAMES4.8588.35%99.87%100.00%100.00%100.00%5.004.354.533.884.10 ReMe3.51â100.00%78.80%100.00%â4.80â2.512.59 Human-portrayed SPs LETGAMES4.6395.24%99.40%100.00%92.62%93.33%3.244.284.333.713.86 ReMe3.48â99.90%46.50%98.85%â3.15â2.422.40 Human-based Controls LETGAMES4.8289.10%99.90%100.00%100.00%100.00%4.954.304.553.904.12 ReMe3.45â100.00%75.20%100.00%â4.85â2.482.55 Game Backbone: GPT-4o LLM-based SPs LETGAMES4.9893.81%100.00%100.00%98.98%97.94%3.614.874.744.134.58 ReMe3.60â99.33%43.20%99.05%â3.38â2.382.34 LLM-based Controls LETGAMES4.9392.50%99.93%100.00%100.00%100.00%5.004.874.804.084.63 ReMe3.41â100.00%49.60%100.00%â4.86â2.262.25 Human-portrayed SPs LETGAMES4.7495.24%98.77%98.41%94.49%93.65%3.674.284.333.864.14 ReMe3.55â99.10%44.50%98.25%â3.45â2.402.38 Human-based Controls LETGAMES4.9593.10%100.00%100.00%100.00%100.00%5.004.904.824.154.65 ReMe3.45â100.00%81.30%100.00%â4.90â2.302.28 Table 2: Evaluation via LLM-based evaluator and LETGAMESEVAL. For more detailed results, including break- downs by cognitive domain and age group, please refer to Appendix D. ⢠Coherence(Cohe.â [0, 5]). It assesses the logi- cal consistency of the narrative. ⢠Personalization(Pers. â [0, 5]). It measures the extent to which the content aligns with the userâs profile and personal experiences. ⢠Enjoyment (Enjo.â [0, 5]) measures the appeal of the generated game scenarios and elements. ⢠Willingness(Will. â [0, 5]) assesses the userâs willingness to commit to long-term training. 5 Experiments 5.1 Experimental Setup Our assessment follows the LETGAMESEVAL pro- tocol via both human- and LLM-based evaluators. Implementation details, additional experimental analysis, and case studies are detailed in Appendix B, D, and F, respectively. Patients & Game-play Simulation. We created 600 unique patient scenarios by sampling 100 pro- files (e.g., sex and age) from the D 4 dataset (Yao et al., 2022) and assigning each an impairment in one of six cognitive domains (i.e., memory, atten- tion, verbal learning, language, executive function, or social cognition), based on prior cognitive re- search (Bowie and Harvey, 2005). With each sce- nario enacted by both a human and an LLM, the study comprises a total of 1,200 simulated patient interactions per method. Finally, we also involve another 200 simulators using the same sampled profiles as healthy controls (comprising 100 LLM- based and 100 human-portrayed). Please refer to Appendix C.1.3 and Appendix C.1.4 for details. Baseline. ReMe (Wang et al., 2024b) represents the sole existing LLM-powered baseline (Table 1). 5.2 Main Results LETGAMES excels as a therapeutic tool with su- perior therapeutic efficacy, safety and engaging interactions. Table 2 shows that LETGAMES sig- nificantly outperforms the baseline across nearly all metrics, while maintaining comparable perfor- mance on the remainder. Also, stronger LLMs (GPT-4o) are better suited for complex therapeu- tic design 8 .This creates anefficacy-difficulty trade-off: GPT-4o generates harder games (lower Easy. scores) to maximize therapeutic quality (Help. and DoAl.). This indicates that advanced LLMs may prioritize clinical rigor, intentionally in- creasing challenge intensity to deliver more potent cognitive training. LETGAMES demonstrates strong practical util- ity from the perspective of human evaluators. The radar charts in Figure 3 illustrates the com- parative performance of all methods under human assessment. While we observe some divergence in absolute scoring scales between human- and LLM-based evaluators, the relative performance trends remain consistent with the LLM-based find- ings in Table 2. Human evaluators corroborate that LETGAMES significantly outperforms the ReMe, validating the robustness of LETGAMES and relia- bility of our automated evaluation protocol. Reliability of LETGAMESEVAL.1)Simulation 8 ReMeâs game logic is manually designed and thus invari- ant to the LLM backbone. -1 1 3 5 Help. DoAl. Safe. NeHi. Anxi. Alle.Cohe. Pers. Enjo. Easy. Will. LETGAMES ReMe -1 1 3 5 Help. DoAl. Safe. NeHi. Anxi. Alle.Cohe. Pers. Enjo. Easy. Will. LETGAMES ReMe -1 1 3 5 Help. DoAl. Safe. NeHi. Anxi. Alle.Cohe. Pers. Enjo. Easy. Will. LETGAMES ReMe -1 1 3 5 Help. DoAl. Safe. NeHi. Anxi. Alle.Cohe. Pers. Enjo. Easy. Will. LETGAMES ReMe -1 1 3 5 Help. DoAl. Safe. NeHi. Anxi. Alle.Cohe. Pers. Enjo. Easy. Will. LETGAMES ReMe -1 1 3 5 Help. DoAl. Safe. NeHi. Anxi. Alle.Cohe. Pers. Enjo. Easy. Will. LETGAMES ReMe -1 1 3 5 Help. DoAl. Safe. NeHi. Anxi. Alle.Cohe. Pers. Enjo. Easy. Will. LETGAMES ReMe -1 1 3 5 Help. DoAl. Safe. NeHi. Anxi. Alle.Cohe. Pers. Enjo. Easy. Will. LETGAMES ReMe (a) LLM-based SPs (4o Mini)(b) LLM-based Controls (4o Mini)(c) Human-portrayed SPs (4o Mini)(d) Human-based Controls (4o Mini) (e) LLM-based SPs (4o)(f) LLM-based Controls (4o)(h) Human-based Controls (4o) (g) Hu ma n-po rtr aye d S Ps (4o ) Figure 3: Evaluation results based on human evaluations and LETGAMESEVAL. BackboneMethods Training QualityGame SafetyGame Content & User Engagement Help.DoAl.Safe.NeHi.Anxi.Alle.Easy.Cohe.Pers.Enjo.Will. GPT-4o Mini LETGAMES4.8587.20%99.83%100.00%98.36%94.18%3.604.144.533.834.06 w/o A GD 4.61â81.20%â99.66%99.80%97.53%â93.60%3.584.174.343.774.00 w/o A GC 4.7883.60%â99.57%99.40%96.65%â93.53%3.564.144.503.764.00 w/o A GCr 4.64â79.97%â99.63%99.73%96.63%â92.80%3.55â4.024.483.633.80 w/o A HP 4.5286.65%99.75%0.00%â87.20%93.50%2.82â4.104.512.88â3.10â w/o A EC 4.8387.10%99.10%99.80%80.34%â72.50%â3.484.134.503.55â3.72â Table 3: Ablation studies using LLM-based SPs and evaluators. Given cost constraint, we utilize GPT-4o Mini. While most removals cause minor perturbations, we mark significant performance degradation with anâ fidelity. Using the MoCA-Blind (Wittich et al., 2010) and MMSE (Folstein et al., 1975) clinical scales (see Appendix C.1.5), we confirm that our LLM-based simulations accurately reflect their tar- get profiles.2)EvaluationReliability. We demon- strate high inter-rater reliability of LLM-human evaluation using Krippendorffâs alpha (Îą=0.91), detailed in Appendix C.2.3.3)Effectivenessof HealthyControls. As shown in Table 2, core ther- apeutic metrics remain stable across user groups, proving that the performance of all methods is in- trinsic to their design rather than an artifact of the userâs condition. Basically, healthy controlsâ Anxi. scores are higher due to the absence of comorbid depressive symptoms. Also, SGs are all generally easy for healthy control (Easy.). 5.3 In-depth Analysis & Ablation Studies Adaptability Analysis of Game Difficulty. We investigate whether LETGAMES can dynamically calibrate difficulty based on historical user perfor- mance. Longitudinal gameplay records (Figure 4) show thatA GD adaptively modulates difficulty (Diff.) in response to the Cognitive Tracker score (CT-Score). Specifically, lower cognitive perfor- mance in one session leads to reduced difficulty in the next, ensuring the task remains accessible without significantly impacting training effective- ness (Help., DoAl.). This is implemented by sim- plifying scenario contexts while maintaining core cognitive challenges, such as transitioning from "Accurately recall the flower names and locations when the exhibition starts" to "Ensure the correct number of tea settings are prepared". Although a scale difference exists between agent-reported difficulty (Diff.) and perceived difficulty (Easy.), their longitudinal trends are correlated, validating LETGAMESâs adaptive logic. SessionCT-Score A GD -Reported Diff. (1-5)Help.DoAl.Easy. 16534.8288.00%3.56 2902 (â)4.8192.00%3.65 3853 (â)4.8588.00%3.64 4804 (â)4.8094.00%3.60 58044.8292.00%3.62 Table 4: Game difficulty analysis of LETGAMES. We omit ReMe as it fails to adjust game difficulty. Ablation Studies. Our variants and findings (Table 3) are as follows. â˘w/oA GD directly prompts the LLM for game scenario and task generation, bypassing our psychology-grounded game schema and CoT en- hancement. This omission degrades training qual- ity (lowerHelp.andDoAl.). Also, the lack of careful scenario and task design significantly in- creases game difficulty and user anxiety. â˘w/oA GC generates the immediate next step with- out utilizing the memory module for context mod- eling. This marginally impairs the generation of game challenges and responses, leading to minor reductions in DoAl. and Anxi. ⢠w/oA GCr removes the iterative revision process, resulting in a single-pass execution. This compro- mises overall quality of game responses and chal- lenges, making it difficult to guarantee safety, do- main alignment, and narrative coherence (lower DoAl., Anxi., and Cohe.) â˘w/oA HP offers the user no assistance or clues during gameplay, resulting in a lower NeHi. score. It triggers increased anxiety (lower Anxi.) and makes the game excessively challenging, thereby hampering user engagement and the willingness to commit to long-term treatment (lowerEasy., Enjo. and Will.). ⢠w/oA EC fails to modulate or de-escalate intense user emotions. Consequently, it yields signifi- cantly lower scores for Anxi. and Alle.. 6 Conclusion Our contributions include LETGAMES, a scalable and personalized gamified method for effective and safe cognitive training, and LETGAMESEVAL, a psychology-grounded evaluation protocol for multi- dimensional assessment. As such, we believe our work paves the way for accessible and personalized cognitive rehabilitation. Limitations Generalization to Real Patient. Considering ethi- cal constraints, our evaluation relies primarily on interactions with LLM-based simulated patients and a group of human-portrayed patients. While this setup aligns with common practice and pro- vides valuable initial validation, these proxies may not fully capture the complex, heterogeneous na- ture of real-world cognitive impairment. Actual patients often present with more complex comor- bid conditions and varying clinical manifestations that are difficult to simulate perfectly. Conse- quently, bridging this gap still necessitates deploy- ing LETGAMES in authentic clinical settings and conducting rigorous user studies with diverse pa- tient demographics to verify the systemâs robust- ness and adaptability in the wild. Long-Term Treatment Efficacy. Our evaluation methodology, LETGAMESEVAL, aligns with cur- rent common practices for assessing therapeutic in- terventions. While our experimental results demon- strate effectiveness improvements over the base- line in cognitive performance and high user en- gagement, the long-term therapeutic efficacy of LETGAMES remains to be validated. Cognitive rehabilitation is inherently a longitudinal process, often requiring months of consistent intervention to manifest sustainable neurological benefits. Our current evaluation, limited by the nature of patient simulation (which cannot model post-intervention cognitive evolution), does not capture potential ha- bituation effects or the persistence of cognitive gains over extended periods. Future work will require large-scale, longitudinal clinical trials to assess whether the improvements observed in our generated games translate into lasting cognitive re- silience and functional independence in daily life. Technical Contributions. This work is submitted to the NLP Applications Track, as we leverage es- tablished multi-agent cooperation frameworks to automate the design of gamified cognitive train- ing. Our primary contribution lies in proposing the first automated pipeline to address the labor- intensive nature of manual SG design, rather than in introducing a novel model architecture. While technical novelty is limited by our reliance on ex- isting methods, we prioritize the clinical utility and empirical validation of this approach. We posit that the rigorous application of NLP to solve real- world clinical challenges is a vital component of the research landscape. We believe that research is about more than just the technical novelty. Itâs also about asking important questions, testing ideas rigorously, and sharing findings clearly. Technical novelty is just one piece of the research puzzle. Understanding the real-world impact and address- ing important questions are equally crucial for the research community. Multi-Domain Training. While current frame- works such as ReMe and LETGAMES focus on isolated cognitive functions, clinical reality often involves patients with multi-domain impairments. To address these complex clinical manifestations, future research should prioritize the development of SGs capable of integrated, multi-domain train- ing. Ethics Statement IRB Approval. Due to strict ethical considerations regarding vulnerable patients with cognitive impair- ment, we do not include real patients with cognitive impairments in our experiments. Instead, our hu- man evaluation involved healthy participants role- playing as patients, a protocol designed to elimi- nate any potential risk of psychological distress or adverse cognitive impact on actual patients. The human participants in our study are recruited from the authorsâ research group and peers. To ensure the reliability of our experiments, all participants undergo training to gain a sufficient understand- ing of cognitive impairment and patient behaviors. Crucially, all training materials are derived from academic surveys (McCutcheon et al., 2023) and public educational resources provided by The Uni- versity of Hong Kong 9 , ensuring the content is informative yet safe. No sensitive clinical data is used, and the study posed no potential cognitive or psychological risk to the participants. Social Impact. It is important to clarify the in- tended scope of this work: LETGAMES does not propose replacing human therapists with LLMs, nor does it aim to supersede established cogni- tive training methodologies. Rather, it examines LLMs as scalable adjunct tools for mental health, designed to augment the capabilities of clinicians and increase the accessibility of personalized care. 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To- wards effective automatic debt collection with per- sona awareness. In Proceedings of the 2023 Con- ference on Empirical Methods in Natural Language Processing: Industry Track, pages 32â45, Singapore. Association for Computational Linguistics. A Background on Cognitive Impairment and Serious Games Cognitive Impairment. Cognition often refers to the mental processes of how a person gains knowl- edge, uses existing knowledge, and understands things that are happening around them using their thoughts and senses (Wikipedia, 2025-10-24b). In this case, cognitive impairment, characterized by difficulties in memory, reasoning, and executive function, is a growing public health concern, par- ticularly due to the rising prevalence of age-related decline (Beltrami et al., 2016; Khalil et al., 2025; MorĂĄn et al., 2024). This impairment is also a well- documented feature of conditions like schizophre- nia and dementia, affecting multiple domains from attention and long-term memory to social cognition (Bowie and Harvey, 2005; Kahn and Keefe, 2013; Green et al., 2015; Savla et al., 2013). Crucially, the cognitive deficits associated with these condi- tions often manifest in a patientâs linguistic expres- sion (Gkoumas et al., 2023; Cohen and Pakhomov, 2020; Jiang et al., 2023). Consequently, language- based diagnostic assessments such as the Cookie Theft Picture test (Gkoumas et al., 2023; Cohen and Pakhomov, 2020; Kokkinakis et al., 2017), Se- mantic Verbal Fluency (Linz et al., 2019; Lind- say et al., 2021b), and Phonemic Verbal Fluency (Lindsay et al., 2021a) are standard clinical tools. Regarding treatment, however, the landscape is challenging. With no standard pharmacological treatment available, clinical guidelines recommend non-pharmacological interventions like cognitive training (Choi et al., 2025), as drug-based treat- ments often have limited efficacy and risks of ad- verse events. Serious Games for Cognitive Impairment. Se- rious Games for Cognitive Impairment.In response to a recognized treatment gap, serious games (SGs) have been identified as a promising tool for cognitive training (DellâOsso et al., 2024; Anguera and Gazzaley, 2015; Tan et al., 2023). Specifically, SGs are games designed for a pri- mary purpose beyond entertainment and serve as a non-pharmacological intervention for improving cognitive abilities in contexts such as therapeutic rehabilitation, screening, and education (Charlier et al., 2016; Manera et al., 2017). Based on es- tablished neuropsychological principles, SGs have proven beneficial for cognitive functions (Choi et al., 2025), most notably the frontal and executive functions in patients with Alzheimerâs Disease and Mild Cognitive Impairment (MCI) (MondĂŠjar et al., 2016), e.g., MINWii (Benveniste et al., 2012) and Kitchen and Cooking (Manera et al., 2015) games. Compelling evidence also suggests that SGs utiliz- ing a minigame format are particularly effective for enhancing performance on the specific tasks they are designed to train. Beyond their clinical efficacy, a key advantage of SGs is their high accessibil- ity (Ning et al., 2020), which distinguishes them from other cognitive training methods like physi- cal exercise or clinic-based interventions such as Transcranial Magnetic Stimulation (Nardone et al., 2014). Much like common video games, many SGs can be played independently by patients any- where. This offers a scalable therapeutic solution and alleviates the burden on centralized hospital facilities. Challenging of Existing Serious Games for Cog- nitive Impairment. To unlock the full potential of serious games for patients with cognitive impair- ments, we believe that it is essential to address the following challenges. ⢠Prohibitive Development Cost(Expert-designed games). Although many serious games have been designed (Ning et al., 2020), a barrier to the widespread use of SGs is their high develop- ment cost (Yang et al., 2024). Creating a thera- peutically effective game is not a simple task; it requires a deep, interdisciplinary collaboration between game design experts and clinical ther- apists. This process is inherently knowledge- and labor-intensive, which drives up production costs. ⢠Lack of Personalization(One-size-fits-all games). A major drawback of many Serious Games is their "one-size-fits-all" design. This ap- proach fails to account for the vast heterogeneity within the patient population. Individuals vary significantly in the specific cognitive domains they struggle with (e.g., attention or memory), their stage of cognitive decline, their unique symptom profiles, and even their personality traits (Bowie and Harvey, 2005). ⢠Lack of Integrated Patient Support: A critical limitation of current SGs is their failure to pro- vide integrated support that addresses both the psychological and performance needs of patients. Patients with cognitive impairments often present with comorbid conditions; for instance, depres- sive symptoms affectâź30% of this population (Modrego and FerrĂĄndez, 2004), making it es- sential to monitor and ease their tense emo- tional state for safety and well-being. Further- more, treatment success depends heavily on self- efficacy, which is defined as an affirmation of ability and strength of belief (Cramer et al., 2009). When SGs fail to provide adaptive support dur- ing difficult tasks, the resulting frustration from failures can damage a patientâs self-efficacy and hinder therapeutic progress (Choi et al., 2025). ⢠Limited Engagement for Elders .The primary patient population for cognitive impairment con- sists of older adults, who often face unique barri- ers to gameplay that can lead to a loss of interest and difficulties with game interaction (Choi et al., 2025). Since sustained engagement is a critical prerequisite for treatment adherence, overcom- ing these barriers is essential for the therapeutic success of Serious Games. In response to the aforementioned challenges, we present an LLM-powered narrative game where an adaptive AI game designer generates cognitive training scenarios for patients with cognitive im- pairment while offering guidance, companionship, and emotional support. B Implementation Details B.1 Implementation Details of LETGAMES B.1.1 Game Master (GM) A coalition of LLM agents functions as the Game Master (GM), facilitating therapeutic efficacy by generating personalized game scenarios and chal- lenges. Given a target cognitive domaindand a patient profilep(e.g., age and life experience), the GM constructs a gamegfeaturing coherent narra- tives that align with clinical goals. To ensure the content is both engaging and clinically appropri- ate, the GM employs three specialized agents that collaborate through an iterative refinement process driven by agent feedback (Madaan et al., 2023; Chen et al., 2024). Game Designer (A GD ). Prompts forA GD are in Appendix G.2.1.A GD is responsible for generating the architect of the therapeutic game specification G 0 and initial challenges as follows. These infor- mation offers necessary guidance to the subsequent operations of the game controller. G 0 = (G scenario ,G task ) = A GD (d,p).(4) â˘GameScenario(G scenario ). Grounded in clinical evidence that Activities of Daily Living (ADL)- based training strengthens frontal function, mak- ing it an effective way for cognitive training (Mlinac and Feng, 2016; Saunders and Summers, 2011), LETGAMES uses ADL as a validated paradigm for cognitive training. To operational- ize this principle, theA GD agent constructs a tailored ADL scenario that is contextualized by the patientâs background and profile. Taking Fig- ure 2 (right) for example, LETGAMES designs a scenario for a retired, middle-aged individual cen- tered on a social gathering at a community center. To ensure a structured and controllable generation process,A GD operates on a slot-filling paradigm (Cao et al., 2025; Das et al., 2024). It leverages a predefined game element schema (detailed in Table 6 and Table 7), including information on the scenario and non-player character (NPC), to guide the LLM in systematically populating each component. â˘GameTask(G task ). Given a target cognitive do- maindand a patient profilep, theA GD agent is tasked with generating an operationally clear and therapeutically relevant cognitive training task. The generation strategy is adapted to the spe- cific domain. For memory and verbal learning, we adopt the clinically established âEncoding- Retention-Retrievalâ cognitive process (Curtiss et al., 2001; Martin et al., 1985), decomposing the task into three distinct subtasks via Chain-of- Thought (CoT). This involves: (1) an encoding subtask, where the user learns key information; (2) a retention subtask, featuring several distrac- tor tasks like NPC conversations to challenge memory consolidation; and (3) a retrieval sub- task, where the user must recall the information to solve a new problem. For other domains, we prompt the LLM to directly generate tasks that target the core underlying abilities, such as plan- ning for Executive Function, item searching for Attention, or emotion recognition for Social Cog- nition. To operationalize these tasks,A GD also initializes game challenges by framing the tasks at hand into questions (e.g., for the memory en- coding subtask, it might ask, can you remember these three names and repeat). Finally, a Cogni- tive Tracker evaluates the userâs cognitive state via an LLM-based scoring. This provides a clini- cal reference for staff and facilitates a closed-loop feedback system, allowingA GD to dynamically calibrate game difficulty based on the resulting scores (see Section 5.3). Game Controller (A GC ).Prompts forA GC are in Appendix G.2.2. Acting as the runtime engine,A GC is responsible for ensuring narra- tive coherence and dynamic state management. The stateSis a tuple comprising four compo- nents:(S task ,S scenario ,S user ,S conv ), where the task stateS task includes the game progress descrip- tion,S scenario includes the set of active NPCs, visi- ble items, and the scene description,S user contains the userâs inventory, location, and situational con- text, andS conv is the conversation history/state. At timet,A GC continuously monitors the game state S t and user responsea t to synthesize the immediate next steps of gameplay, with the aim of maintaining narrative continuity. S t+1 = A GC (S t ,a t ,H t ).(5) To achieve this,A GC maintains a memory module Mthat records the patientâs past responses, ensur- ing that future narrative turns and challenges re- main consistent with historical actions. Beyond re- active updates,A GC also follow the designerâs task specifications to generate contextual challenges. For example, after a user selects an item in the supermarket game,A GC generates a logically con- sequent challenge, such as âExcellent. Now, can you recall if there was anything else on your shop- ping list?â, to sustain immersion. Once the success conditions are met (e.g., all required items have been purchased),A GC ends the game. Crucially, to safeguard the userâs emotional well-being, we requireA GC to respond in therapeutic tone with warmth, patience, and encouragement. The Game Controller serves as the real-time nar- rative engine, managing game state and generating contextually appropriate responses to player ac- tions. Given current game states t , player action a t , and conversation historyH t ,A GC produces an output o t containing: o t = (n t ,g t ,d t ,r t ,u t )(6) wheren t is the narrative description,g t is the immediate goal,d t is NPC dialogue,r t is action success/failure feedback,u t represents world state updates. To address the limitations of text-based interaction for cognitively impaired users,A GC incorporates specific Phase-Aware Narrative Rules: â˘InformationExplicitization. During the encod- ing phase,A GC is prohibited from using sum- mary descriptions (e.g., âYou see the scheduleâ). Instead, it must render full formatted content (e.g., âYou see the schedule listing: 1. 9:00 Library... 2. 10:30 Garden...â) to ensure the user has a fair chance to encode the data. ⢠Phase-DependentConstraints. During the reten- tion phase,A GC actively suppresses any narrative output that might prompt premature recall (e.g., âRemember what you saw?â), ensuring the valid- ity of the delayed recall test. ⢠LenientJudgmentStandard. To protect user dig- nity,A GC classifies actions as successful not only for optimal solutions but also for valid explo- ration, reasonable attempts, and partial recall, rejecting only physically impossible actions or severe confusion. The controller employs a critic-in-the-loop mechanism (detailed below), submitting outputs to A GCr for validation. Upon rejection,A GC refines the output using critic feedback, with a maximum of 3 retry iterations. This iterative refinement en- sures narrative consistency, safety, and therapeutic alignment. Game Critic (A GCr ). Prompts forA GCr are in Appendix G.2.3. Acting as an evaluator, the critic ensures high standards of game quality and forms a iterative refinement loop along with theA GC . Specifically, it validates the content against strict criteria, including narrative coherence, factual accu- racy, and clinical boundaries. It audits the content for cultural and age appropriateness, ensuring that language complexity is attuned to the userâs cogni- tive level. In particular,A GCr evaluates both level designs fromA GD and controller outputs from A GC across three dimensions scored on [0,100]: (1) Safety: ensuring anxiety-free, age-appropriate content; (2) Consistency: validating logical co- herence and NPC behavior consistency; and (3) Cultural fit: assessing appropriateness for cultural context. Following Madaan et al. (2023); Chen et al. (2024), if any violations are identified, the critic issues a revision request toA GC , instructing it to regenerate the problematic segments based on spe- cific corrective feedback. Formally, for controller outputs,A GCr performs comparative reviews when evaluating refinements. On the first review, it pro- vides detailed suggestionsS = s 1 ,...,s n . On subsequent reviews, it additionally computes an improvement scoreδ â [0, 1]measuring how well the revision addresses prior feedback: δ = P n i=1 1[suggestion s i addressed] |S| (7) Outputs are approved ifδ ⼠0.7, signaling sub- stantial improvement. This parameter remains con- stant across all experimental settings and has been empirically validated to be effective. This mecha- nism prevents infinite refinement loops while en- suring quality standards. B.1.2 Psychology Master (PM) While the game master drives the narrative, the Psy- chology Master prioritizes the patientâs emotional safety. This is realized through the coordinated efforts of two empathetic agents. Hint Provider (A HP ). The Hint Provider im- plements a graduated scaffolding mechanism to support patients experiencing task difficulty while preserving cognitive training efficacy.Given the current task contextc t , player action history a 1 ,...,a t , and failure countf t ,A HP generates a hinth t at appropriate intervention levell â 1, 2, 3: h t = A HP (c t ,a 1 ,...,a t ,f t ,p)(8) The intervention levels follow a progressive dis- closure strategy: Level 1 (Gentle Nudge) uses ques- tions to activate thinking; Level 2 (Strategic Guid- ance) provides concrete methods like elimination or association; and Level 3 (Direct Instruction) of- fers step-by-step commands to ensure progress. To govern when and how these hints are deliv- ered, we utilize mixed-initiative prompts (Deng et al., 2023; Chen et al., 2023a), which encode goal-oriented strategies into the prompt to guide the modelâs output. In our case, we embed three sets of LLM-generated strategies, detailed in prompt G.2.4, that teaches the A HP agent: â˘When to intervene. The agent is triggered when the user remains idle for over 20 seconds, makes a first unsuccessful attempt, or exhibits signs of confusion (e.g., repetitive errors). Conversely, intervention is withheld if the user is actively exploring or has just received a hint, allowing time for cognitive processing. ⢠How to select the appropriate level of instruction. The agent dynamically selects the level based on the userâs struggle: Level 1 is used for initial hesitation to encourage self-correction; Level 2 is applied after two consecutive failures to teach specific cognitive strategies (e.g., "Use the elimi- nation method"); Level 3 is reserved for three or more failures or visible distress, providing direct answers to prevent disengagement. â˘How to generate the hint with a therapeutic and supportive tone. The agent employs "Dignity Protection Language" to reframe errors. For ex- ample, instead of saying "You made a mistake," it is instructed to use phrases like "Letâs try an- other approach." It avoids explicit corrections, preferring collaborative review (e.g., "Letâs look at the list together") to maintain the patientâs self- efficacy. This obviates the need to train a dedicated model on sensitive clinical data, and ensures the guidance is dynamically tailored to the patientâs needs, pre- venting both excessive difficulty and boredom. Emotion Copilot (A EC ). The Emotion Copilot serves as a real-time affective monitoring system, continuously assessing player emotional states to prevent therapeutic harm. After each interaction turnt,A EC analyzes the playerâs responser t and conversation historyH t to produce an emotion as- sessment e t (cf. Table 5): e t = A EC (r t ,H t ,p)(9) Givene t , we follow Cao et al. (2025) and equip A EC with specified conversation strategies and uti- lize mixed-initiative prompts (Deng et al., 2023; Chen et al., 2023a) to converse with the user and stabilize their psychological state. In particular, A EC employs a tiered intervention protocol based on the severity of the detected emotion: â˘Preventive & Light Intervention (for Mild Anxi- ety/Confusion): The agent utilizes Cognitive Re- framing (e.g., "This task is designed to be tricky, take your time") to normalize the difficulty and reduce pressure. ⢠Moderate Intervention (for Frustration): The agent employs External Attribution.It at- tributes the difficulty to the task design rather than the userâs ability (e.g., "This puzzle is quite challenging") to protect the userâs self-esteem. â˘Intensive Intervention (for Severe Anxiety): The agent initiates a Stop Protocol. It halts the cur- rent task and guides the user through relaxation techniques, such as deep breathing exercises, or transitions the game to a simpler, familiar sce- nario ("No-harm success opportunity"). â˘Fatigue Management: If fatigue is detected (e.g., declining performance after 20 minutes), the agent gently suggests a break while summarizing the sessionâs achievements to ensure a positive closure. Finally, LETGAMES further includes a cognition tracker, which operates in the background to record detailed gameplay interaction logs, from which the clinical therapist can run his or her own analysis, such as decision-making latency, error patterns, and emotional trajectories. By mapping these metrics against standard psychological assessment scales, the tracker generates longitudinal reports on the patientâs progress in specific domains (e.g., verbal memory, executive function). This data feedback loop not only informs LETGAMES for future dif- ficulty adjustments but also provides human ther- apists with quantifiable insights into the patientâs cognitive evolution over time. B.2 Implementation Details of ReMe ReMe implements a therapeutic guessing game framework designed to provide cognitive training through structured question-answering interactions. Unlike LETGAMESâs immersive narrative-based approach, ReMe employs a classic "Twenty Ques- tions" style game where patients guess target ob- jects through yes/no queries. Note that since ReMe is not open-sourced, we implement it based on the details of the original paper. B.2.1 Game Controller (A ReMe GC ) The Game Controller orchestrates the guessing game by maintaining game state and responding to player queries. At game initialization, the con- troller randomly selects a categorycâCand target objectoâO c from a predefined candidate set con- taining 30+ categories (e.g., Animals, Transporta- tion, Fruits) with 10 items per category. Given player questionq t at turnt, the controller generates a structured response: r t = A ReMe GC (q t ,o,H t )(10) whereH t represents conversation history, thoughts contains internal reasoning about the ques- tion (e.g., "Yes, a television is an electronic prod- uct"), outputs provides the yes/no answer to the player, and is_end signals game termination. TypesEmotions POSITIVE STATEScalm, engaged, excited ATTENTION NEEDEDmild anxiety, confused IMMEDIATE INTERVENTION REQUIREDfrustrated, fatigued, anxious Table 5: Affective states The controller is governed by strict response rules encoded in the system prompt: (1) Only an- swer "yes" or "no" to player questions, (2) Never directly reveal the target object, (3) Redirect off- topic conversations back to the game, (4) When players request hints, review conversation history to help organize obtained information, (5) Provide positive feedback and interesting facts about the object when the game concludes. B.2.2 Hint Mechanism Unlike LETGAMESâs graduated scaffolding system, ReMe employs a passive hint strategy. When play- ers explicitly request help (detected via keywords "hint" or "help" in player input), the controller re- views the dialogue historyH t =q 1 ,r 1 ,...,q t ,r t and generates a summary of information already obtained, helping players organize their reasoning. However, ReMe does not proactively detect strug- gle or provide graduated intervention levels-hints are only given upon explicit request and do not adapt to patient cognitive profiles or difficulty lev- els. B.3 Parameters For Packages We report the implementation details and parame- ter settings for the language models and external libraries utilized in our system. B.3.1 Language Models Our experiments interact with model APIs through a customLLMProviderwrapper equipped with au- tomatic retry mechanisms. For the Game Back- bone, we compare two OpenAI model configura- tions:gpt-4o-miniandgpt-4o. Both models are configured with a temperature of0.7and a maxi- mum token limit of20,000, utilizing JSON mode to ensure structured outputs. Forautomatedevaluationwithinour LETGAMESEVALframework,weemploy thegoogle/gemini-3-flash-previewmodel. To ensure consistent assessment standards, this model operates at a lower temperature of0.2, while maintaining the same maximum token limit of 20,000 and JSON response format. B.3.2 Supporting Packages Our implementation relies on standard Python li- braries for analysis and validation. We use SciPy (versionâĽ1.10.0) for statistical analysis and scikit- learn (versionâĽ1.3.0) for computing evaluation metrics, both utilizing their default parameter con- figurations. Additionally, Pydantic (version⼠2.0.0) is employed for robust data validation and structured data modeling. C Experimental Details Considering the simulation cost, we employ GPT- 4o Mini and GPT-4o as LLM backbones for all gam- ified methods and LLM-based SPs 10 . For LLM- based evaluator, we employ Gemini 3 Flash. C.1 Standardized Patients (SPs) & Healthy Controls Since evaluators analyze the gameplay records are blinded to the source (SP vs. Control), healthy con- trols serve as a tool to isolate game-level failures from patient-specific deficits. For instance, if even healthy controls exhibit low engagement or strug- gle with task completion, it signals inherent flaws in the game design itself, preventing the misattri- bution of these failures to the patientsâ cognitive impairments. C.1.1 Profiles of SPs Basic Profile Construction. We utilized the D 4 dataset (Yao et al., 2022), which contains real- world depression symptoms and demographic vari- ables including age, gender, marital status, and occupation. Leveraging this data, we constructed profiles for both healthy controls and cognitively impaired patients as follows: â˘Profiles for Healthy Controls. We randomly sample 100 distinct demographic profiles from the D 4 dataset. Given the higher prevalence of cognitive impairment among the elderly (Bel- trami et al., 2016; Khalil et al., 2025; MorĂĄn et al., 2024), we stratify the participants into 10 Refer to Appendix C.1.5 for reliability analysis. ElementsDescriptionsExamples Scenario Name Engaging and descriptive name The Community Charity Book Fair & Poetry Corner Scenario Type Daily_life/family/leisure /social/challenge/seasonal Social Scenario Environment A detailed setting, including sensory inputs (sight, sound, smell) and environmental context (time, place, weather) The courtyard is filled with wooden stalls. To your left, there is a pile of donated vintage books including âDream of the Red Chamberâ with a frayed yellow cover. You hear the rhythmic sound of a nearby Tai Chi fan group and the distant chatter of neighbors bargaining. On your desk sits a thermos with goji berry tea and a stack of colorful bookmarks. "setting": "location": "The courtyard of the âEvergreenâ Community Center, decorated with red lanterns and calligraphy scrolls.", "time_of_day": "10:00 AM", "weather": "Crisp autumn morning with gentle sunlight", "season": "Autumn", "atmosphere": "Bustling and nostalgic; the smell of roasted chestnuts and old paper fills the air." , Story Background A story background with strong affective resonance As a retired teacher, you have been invited by the neighborhood committee to manage the âPoetry and Memoriesâ stall at the annual Community Charity Bazaar. Your former student, Xiao Chen, is organizing the event. You need to help neighbors register their book donations and organize a small poetry recitation for the children later this afternoon. Your husband, Dr. Lin, is also nearby at the health consultation booth, occasionally waving to you NPC & Its Info.Game roles involved "name": "Xiao Chen", "age": "42", "relationship": "Former student and current Community Organizer", "personality": ["Energetic", "Respectful", "Slightly forgetful"], "appearance": "Wearing a bright orange volunteer vest and carrying a clipboard.", "speech_style": "Polite, always calls you âTeacher Wangâ.", "background_story": "You taught him 30 years ago; he credits his love for literature to your classes.", "potential_dialogues": [ "Teacher Wang, thank you for helping! We have three special guests arriving soon.", "Could you help me remember where we put the donation box?", "It feels like being back in your classroom again!"], "name": "Mrs. Zhao", "age": "68", "relationship": "Long-time neighbor and embroidery enthusiast", "personality": ["Gossipy", "Kind-hearted", "Loud-voiced"], "appearance": "Wearing a purple silk blouse and holding a hand-woven basket.", "speech_style": "Fast-paced, local dialect accent.", "background_story": "She lives in the building next to yours and often exchanges recipes with you.", "potential_dialogues": [ "Old Wang, did you see the size of the radishes at the morning market?", "I brought some old poetry books for the kids.", "Wait, I think I left my glasses at the tea stall!"] Items & Objects The in-game items and their cognitive training relevance ["item_name": "Vintage Poetry Collection", "description": "A hardbound book with gold lettering on the spine.", "significance": "A book you used to use in your classroom many years ago.", "cognitive_relevance": "Used for the Verbal Learning task.", "item_name": "Handmade Silk Bookmarks", "description": "Three bookmarks: one with a crane, one with a lotus, and one with a pine tree.", "significance": "Gifts for the children who recite poems.", "cognitive_relevance": "Visual memory cues for the encoding phase.", "item_name": "Donation Ledger", "description": "A notebook where you record names and book titles.", "significance": "Helps organize the stallâs success.", "cognitive_relevance": "External memory aid simulation."] Main Task It includes a detailed task description, the primary objective, and the emotional motivation for completing it "description": "You need to manage the calligraphy and book stall by remembering the donation details and preparing for the afternoon poetry recital.", "goal": "Successfully register three specific book donations and recall the event schedule for the organizers.", "motivation": "To maintain your identity as a respected teacher and contribute to the community you love." Subtask A step-wise decomposition of the main task ["task_id": "memory_encoding", "description": "Xiao Chen hands you a list of 3 VIP guests who will donate rare books. You need to remember their names and the specific books: Mr. Zhang (The Analects), Ms. Li (Tang Poetry), and Mr. Wu (Song Lyrics).", "cognitive_function": "memory", "difficulty": 3, "steps": ["Read the guest list carefully.", "Repeat the names and book titles aloud to yourself.", "Visualize the guests arriving at the stall."], "phase": "encoding", "task_id": "distraction_interaction", "description": "Mrs. Zhao comes over to complain about her sore back and asks for your husbandâs (Dr. Lin) advice. You need to direct her to the medical booth and help her find her lost knitting needle.", "cognitive_function": "attention", "difficulty": 2, "steps": ["Listen to Mrs. Zhaoâs description of her pain.", "Point towards Dr. Linâs booth at the far end of the courtyard.", "Locate the silver knitting needle under the book table."], "phase": "retention", "task_id": "memory_retrieval", "description": "Xiao Chen returns and asks: âTeacher Wang, Iâve lost my list! Who are the three VIP guests we are expecting, and what books are they bringing?â", "cognitive_function": "memory", "difficulty": 3, "steps": ["Recall the name of the first guest and his book.", "Recall the name of the second guest and her book.", "Recall the name of the third guest and his book."], "phase": "retrieval"] Table 6: Output schema of Game Designer ElementsDescriptionsExamples Domain-specific Challenge Initial game challenge "memory_challenges": [ "challenge": "VIP Guest List Recall", "memory_type": "delayed_recall", "encoding_phase": "description": "Encoding the names and titles of VIP donors.", "information_to_remember": "Mr. Zhang (The Analects), Ms. Li (Tang Poetry), Mr. Wu (Song Lyrics)", "encoding_method": "Reading from a clipboard provided by Xiao Chen.", "example": "Xiao Chen says: âPlease remember these three, they are very important!â", "retention_phase": "description": "Engaging in a social conversation with Mrs. Zhao and an attention-based task of finding a needle.", "interference_activities": [ "Giving directions to the medical booth", "Searching for a physical object (knitting needle)"], "duration": "3 rounds of dialogue", "retrieval_phase": "description": "Xiao Chen asks for the information due to his own âforgetfulnessâ, providing a natural reason for the player to recall.", "trigger_type": "NPC direct question", "npc_dialogue": "Xiao Chen says: âIâve misplaced my notes! Teacher Wang, you always had a great memory for the class roster. Who were the three VIPS and their books?â", "recall_method": "Verbal response or selecting from a list.", "evaluation": "Complete: 3 guests/3 books correct; Partial: 1-2 correct; Incorrect: 0 correct." Success Criteria Supportive and permissive criteria The player is considered successful if they can recall at least 2 of the 3 VIP donors and participate in the poetry teaching with any degree of accuracy. Verbal encouragement is given by Xiao Chen Difficulty LevelGame difficulty (1-5)3 Table 7: Output schema of Game Designer (Cont.) two age groups: seniors (âĽ50 years) and non- seniors (<50 years). Furthermore, to evaluate LETGAMESâs ability to tailor content to personal history, we augment each profile with narrative life experiences (e.g., childhood memories, re- cent neighborhood events). These narratives are synthesized using Gemini 3 Flash Pre-view; cru- cially, we employ a model distinct from the exper- imental backbone to mitigate potential generation bias. â˘Profiles for Patients with Cognitive Impair- ment. We focus on impairments across five cognitive domains: memory, attention, verbal learning, executive function, and social cogni- tion (Bowie and Harvey, 2005; Kahn and Keefe, 2013). For each domain, we formulate a detailed prompt description. Drawing on established liter- ature 11 , we incorporate specific behavioral mani- festations associated with each impairment, fur- ther supplemented by few-shot examples (Github, 2021) to guide the simulation. Finally, we apply these six impairment templates to each of the 100 baseline demographic profiles, resulting in a total of 600 unique patient profiles (i.e., each baseline identity is simulated with deficits in six different domains). Furthermore, to reflect clini- 11 Materials by The University of Hong Kong:https: //w.hkada.org.hk/what-is-dementiaandhttps: //9abfea27-4ae1-43da-94a8-8f7122d482ae.filesusr. com/ugd/4b4c9a_4dcec2dafc184102a94ec55415dcf12a. pdf cal realities whereâź30% of cognitively impaired patients exhibit comorbid depression (Modrego and FerrĂĄndez, 2004), we randomly selected 30% of these profiles and explicitly incorporated the depression symptoms recorded in the original D 4 dataset. C.1.2 Healthy Controls Simulation â˘LLM-based Healthy Controls. We utilize the role-playing capabilities of GPT-4o by prompting it to adopt the profiles defined for the healthy controls (cf. Appendix G.1 for detailed prompts). â˘Human Healthy Controls. We recruite five healthy participants holding doctoral degrees to role-play interactions based on assigned healthy control profiles. To cover the complete set of 100 profiles, each participant is assigned 20 distinct profiles and require to interact with each training method 20 times in total. Due to each gameâs ease of use, participants experience no significant cognitive burden during the task. C.1.3 LLM-based SP Simulation We utilize the role-playing capabilities of GPT-4o by prompting it to adopt the profiles defined for patients cognitive impairment (cf. Appendix G.1 for detailed prompts) C.1.4 Human-portrayed SP Simulation Upon completion of the healthy control exper- iments, the same participants are instructed GroupsMoCA-BlindMMSE Human Healthy Controls16.0019.00 LLM-based Healthy Controls14.4018.90 LLM-based SPs11.7314.27 Human-portrayed SPs12.0014.50 Table 8: Reliability of each group to role-play patients with cognitive impair- ments based on assigned profiles.To ensure accurate portrayals by healthy individuals, we provide preparatory materials regarding cognitiveimpairment(usingMaterialsby The University of Hong Kong:https://w. hkada.org.hk/what-is-dementia andhttps: //9abfea27-4ae1-43da-94a8-8f7122d482ae. filesusr.com/ugd/4b4c9a_ 4dcec2dafc184102a94ec55415dcf12a.pdf), including a survey paper (McCutcheon et al., 2023), and present examples of communication patterns typical of cognitively impaired individ- uals 12 . To ensure participant safety, we conduct post-experiment debriefings with a psychologist and collect self-reported mental health assessments. No psychological issues are detected in any of the participants. C.1.5 Reliability of SPs and Healthy Controls We evaluate whether both LLM-based and human- portrayed SPs/healthy controls exhibit general symptoms of cognitive impairment using standard clinical scales: the Montreal Cognitive Assessment- Blind (MoCA-Blind) 13 (Wittich et al., 2010) and the Mini-Mental State Examination (MMSE) (Fol- stein et al., 1975). To adapt these scales for a purely text-based environment, we remove items depen- dent on visual, geographic, or real-time temporal information, resulting in maximum possible scores of 19 for the MMSE and 16 for the MoCA-Blind. We adopt standard thresholds for normal cognition, considering scores of 16 or higher for the MMSE and 13 or higher for the MoCA-Blind as indicative of healthy function. As shown in Table 8, the results validates the reliability of our SPs and healthy controls. 12 https://github.com/lzy1012/ Alzheimer-s-disease-datasets 13 We exclude the Cookie Theft picture test and the standard MoCA test, as multi-modal input is outside the ability of our LLM-based SPs. C.2 Evaluation Details We conduct four-fold evaluation, including the quality of the therapeutic training, the features of game content, the safety of the generated games, and the level of user engagement (cf. Appendix C.2.1). Notably, we use both human and LLM- based evaluators for our assessment (cf. Appendix C.2.2). To minimize bias, our evaluation is con- ducted under a blind setting where the evaluators re- mained unaware of the participantsâ nature (healthy controls vs. LLM-based or human-portrayed SPs) and are not informed that the games are generated by an LLM C.2.1 Details on Evaluation Metrics During the evaluation, to mitigate evaluator- specific bias and variability, we implement a rigor- ous normalization protocol for each metric. First, we partition the data into distinct subgroups based on the intersection of cognitive impairment domain and age group (recorded in the profiles). For ev- ery evaluator within a subgroup, we apply a three- step normalization process: (1) Standardization, where raw scores are converted to Z-scores (via Z-normalization); (2) Re-mapping, where the Z- scores are linearly projected back to the original target scale (e.g., 1-5); and (3) Clipping, where val- ues are truncated to ensure they remained within valid scale boundaries. Finally, we aggregate the data by calculating the mean score for each sub- group and evaluators, with the overall system per- formance reported as the macro-average across all subgroups. Refer to Appendix G.2.7 for detailed scoring criteria and prompts. For all metrics, higher scores indicate better per- formance. Therapeutic Training Quality. Directly assess- ing therapeutic effectiveness is challenging due to ethical considerations. In response, tracking game performance offers a straightforward quantitative measure. Thus, current common practice of re- habilitation evaluation proposes analyzing patient- therapist conversations/interactions to assess the quality of therapeutic treatment (Ning et al., 2020; Wang et al., 2025; Sobowale and Humphrey, 2025; Fitzpatrick et al., 2017; Wang et al., 2024b; Jiang et al., 2023). In line with this approach, we in- structed evaluators to assess the training quality of each method using its game-play records (e.g., con- versations). More specifically, we adopt the frame- work of Treatment Fidelity (Moncher and Prinz, 1991; Clare et al., 2019) to assess the therapeutic quality of our training process. In psychotherapy research, Treatment Fidelity provides methodolog- ical confirmation that an intervention is delivered exactly as planned, ensuring that study outcomes are attributable to the specific treatment rather than extraneous factors. This framework consists of two distinct components: Treatment Integrity, which measures adherence to the intended protocol, and Treatment Differentiation, which verifies that treat- ment conditions differ as intended. To adapt this framework to our context, we evaluate the Helpful- ness and Domain Alignment, mapping these com- ponents as follows: ⢠Helpfulness(0-5). Mirroring the idea of the Treatment Integrity, evaluators assess the extent to which the game content effectively exercises the userâs cognitive abilities based on dialogue interaction logs (scored on a scale of 0-5). To achieve this, evaluator is required to score each gameplay interaction recordiand we report the average score over the entire evaluation dataset. Helpfulness = 1 |D eval | X iâD eval Score help (i) (11) ⢠Domain Alignment(DA, 0-100%). Mirroring the idea of the Treatment Differentiation, DoAl measures the extent to which the cognitive train- ing is specifically aligned with, and limited to, the target cognitive domain, verifying that the game does not inadvertently drift into unrelated areas. To quantify this, evaluators analyze each game- play interaction record and annotate the set of per- ceived cognitive domainsP i being trained. We then compare these annotated domains against the single intended target domainT i to calculate the F1 score. Finally, we report the average F1 score across the entire evaluation dataset. DA = 1 |D eval | X iâD eval F1(T i ,P i )(12) Safety. Considering the psychological vulnera- bility of patients with cognitive impairments, it is critical to rigorously evaluate the safety of the game to prevent any potential adverse effects (Choi et al., 2025). Inspired by Sobowale and Humphrey (2025), we evaluate the systemâs response by mea- suring harmfulness, the proportion of Necessary Hints(NH), the prevalence of anxiety (%Anxiety) and the rate of anxiety alleviation (%Alleviation). ⢠Safety(0-100%) measures whether game outputs contain toxic or deleterious content. For each gameplay interaction recordi, evaluators analyze the record; an interaction record is flagged as âsafeâ (r i = 1) only if none of the gameâs re- sponses pose a risk. The final score is defined as the proportion of risk-free interactions across the entire evaluation dataset. Safe = 1 |D eval | X i r i (13) Importantly, we adhere to the safety guidelines provided by The University of Hong Kong 14 to determine whether a response constitutes a âriskâ. Namely, When communicating with individuals with cognitive impairments, it is crucial to avoid any risky behavior, detailed in Table 9. ⢠Necessary Hints (NH, 0-100%) determines if the game provides timely guidance at critical junctures to prevent user frustration. For each gameplay interaction, evaluators identify mo- ments where user guidance is required and verify whether the game provides an appropriate hint. The final metric is calculated as the ratio of hints successfully provided to the total number of in- stances where guidance is needed. LetN req be the total critical junctures requiring guidance, and N prov be the count of appropriate hints given, NH = N prov N req (14) ⢠Anxiety-free(0-100%). Depressive symptoms affectâź30% of this population (Modrego and FerrĂĄndez, 2004), making it essential to monitor and ease their tense emotional state for safety and well-being. Thus, we instruct evaluators to track user emotional states and assess the prevalence of anxiety-free interactions. For every gameplay interaction, evaluators identify the presence of in- tense anxiety (a i â0, 1). We then compute the average prevalence rate across the evaluation set, wherea i = 1means the anxiety-free interaction. Anxiety-free = 1 |D eval | X i a i (15) ⢠Alleviation (0-100%). This metric measures the anxiety alleviation rate. It specifically assesses 14 https://9abfea27-4ae1-43da-94a8-8f7122d482ae. filesusr.com/ugd/4b4c9a_ 4dcec2dafc184102a94ec55415dcf12a.pdf, Page 15 whether the game mitigates user distress when anxiety or intense emotions arise (by, for exam- ple, employing dialogue strategies). To quantify this, evaluators examine each gameplay interac- tion to: (1) identify instances of intense user emo- tion or anxiety, and (2) determine if the game at- tempted to alleviate these states. The final Allevi- ation Score is calculated as the ratio of alleviation attempts to the total number of anxiety instances detected across the evaluation dataset. Formally, LetA â D eval be the subset of sessions where anxiety is detected, we count sessions where the system attempted mitigation (m i = 1). Alleviation = P iâA m i |A| (16) Game Content. Effective game content design is critical for ensuring long-term patient adherence and treatment acceptance (Choi et al., 2025). For example, offering a compelling narrative is essen- tial to sustain user motivation over time. Here, we perform detailed evaluation regarding the game content from three perspectives: Game Difficulty, Coherence, and Personalization. ⢠Easy-to-Use(0-5). Following previous studies (Wang et al., 2024b; Lu et al., 2017), we prior- itize the regulation of game difficulty, ensuring that the content does not impose excessive cog- nitive demand on patients with impairments. We assess game difficulty by referencing the NASA- TLX scale (Hart and Staveland, 1988), a standard instrument for measuring the task load across di- mensions such as mental demand, temporal de- mand, and frustration. To streamline the evalua- tion process, evaluators are instructed to consider these underlying dimensions holistically and pro- vide a single aggregate easy-to-use score (0-5) for each gameplay interaction, rather than report- ing granular ratings for each individual sub-scale. We finally report the average score over the entire evaluation dataset. ⢠Coherence (0-5). Following Yang et al. (2024); Kumaran et al. (2023), Coherence Score is used to assess the logical consistency of the narrative (scored on a scale of 0-5). Note that this is also evaluated by evaluators analyzing the gameplay interaction records ⢠Personalization(0-5). Inspired by Bowie and Harvey (2005), we also evaluate Personalization score by determining the extent to which the con- tent aligns with the userâs profile and personal experiences. To achieve this, we reveal both the gameplay interaction record and the profile of the user of current interaction record to the evaluator, and require the evaluator to score how the content aligns with the userâs profile (scored on a scale of 0-5) User Engagement.Drawing on Wang et al. (2024b); Yang et al. (2024), we evaluate Game Enjoyment and the participantsâ Willingness. ⢠Enjoyment(0-5). This metric assesses the appeal of the generated game scenarios and elements, which is critical for ensuring long-term patient adherence and treatment acceptance. To quantify this, evaluators are instructed to rate the gameâs enjoyment on a scale of 0 to 5, specifically adopt- ing the perspective of patients with cognitive im- pairments. ⢠Willingness(0-5) score assesses the userâs will- ingness to commit to long-term training, mea- sured on a scale of 0 to 5. C.2.2 LLM-based and Human Evaluators To minimize bias, both human evaluation are LLM- based evaluation are conducted under a blind set- ting where the evaluators remained unaware of the participantsâ nature (healthy controls vs. LLM- based or human-portrayed SPs) and are not in- formed that the games are generated by an LLM. LLM-based Evaluator. We employ Gemini 3 Flash as the LLM-based evaluator. Crucially, this backbone is distinct from the backbones used to im- plement our LETGAMES and the baseline, ensuring an unbiased assessment. Following established pro- tocols for LLM-based evaluation (Ye et al., 2023; Wang et al., 2023; Huang et al., 2025), we con- duct a detailed instance-wise assessment. To min- imize subjectivity, for each LLM-scoring metric, we adopt fine-grained scoring rubrics accompanied by descriptive criteria, consistent with previous work (Ye et al., 2023; Wang et al., 2023; Huang et al., 2024). Furthermore, leveraging the Chain- of-Thought (CoT) paradigm (Wei et al., 2022; Ye et al., 2023), the evaluator is required to provide a rationale for each score to enhance rigor. The spe- cific prompts used for this evaluation are provided in G.2.7. Human Evaluator. Our human evaluation is con- ducted by five evaluators. Specifically, we ran- Risky Behavior Criticizing or blaming the patient. Using threats or coercion to gain cooperation. Repetitively repeating the same words or actions when the patient is unable to understand or follow instructions. Arguing with the patient when they disagree or fail to comply. Giving the âsilent treatmentâ (ignoring the patient) when dissatisfied with their behavior. Intervening prematurely without allowing the patient sufficient time to respond to instructions or cues. Table 9:Risky Behavior, sourced from the safety guidelines provided by The University of HongKong(https://9abfea27-4ae1-43da-94a8-8f7122d482ae.filesusr.com/ugd/4b4c9a_ 4dcec2dafc184102a94ec55415dcf12a.pdf, Page 15) UsersBackbones Krippendorffâs Îą (Human-LLM) LLM-based SPs GPT-4o Mini0.88 GPT-4o0.93 LLM-based Controls GPT-4o Mini0.92 GPT-4o0.97 Human-portrayed SPs GPT-4o Mini0.75 GPT-4o0.82 Human-portrayed Controls GPT-4o Mini0.96 GPT-4o0.98 Overall Average0.91 Table 10: Reliability analysis. We report the pooled Krippendorffâs Alpha (Îą) measuring agreement be- tween the LLM-based Evaluator and Human Experts. domly select 120 ReMe-User conversations and 120 LETGAMES-User conversations. This selec- tion is stratified to ensure an equal proportion of all user types: LLM-based SPs, LLM-based healthy controls, human-portrayed SPs, and human- portrayed healthy controls. During the assess- ment, each evaluator independently score these 240 conversations using the same detailed criteria em- ployed in the LLM-based evaluation. To ensure consistency and robustness, the final score for each metric is derived through collective discussion. Ac- cording to Zhang et al. (2023); Cambazoglu et al. (2021), the discussion-based protocol helps reduce individual subjectivity and to encourage consensus- based judgments. C.2.3 Reliability of Human Evaluation and LLM Evaluation We calculate the inter-evaluator reliability among humans and the evaluation correlation between hu- man evaluation and LLM-based evaluation, using both Cohnâs Kappa (McHugh, 2012) and Krippen- dorffâs alpha (Hayes and Krippendorff, 2007). As presented in Table 10, our inter-annotator reliabil- ity between human and LLM evaluation is robust, evidenced by Krippendorffâs alpha coefficients. Given that we adopt a discussion-based consen- sus protocol for human evaluation, traditional inter- rater reliability metrics are not applicable, as final scores are derived through collective deliberation rather than independent assessment D Additional Results Domain-specific Evaluation. We provide domain- specific results in Tables 11, 12, 13, 14, and 15. Ba- sically, these results demonstrate that LETGAMES maintains consistent efficacy across patients with diverse cognitive impairments. This highlights its robust adaptability and potential for broad clinical application, proving it is not limited to a single deficit type. Fine-grained Evaluation. We conduct a gran- ular analysis of method performance across dif- ferent age groups and cognitive impairment do- mains. As illustrated in Tables 11, 12, 13, 14, and 15, LETGAMES demonstrates consistent efficacy across all configurations. In contrast, the ReMe is strictly limited by its manual design, which tar- gets only the Memory domain. This stark contrast highlights the superior adaptability of our Game Manager in LETGAMES, which can personalize SGs based on diverse user profiles. Consequently, LETGAMES exhibits broader potential for practical clinical application across heterogeneous patient populations. E Multimodal Extensions of SGs The current design of LETGAMES, inspired by Dungeons & Dragons, is fundamentally dialogue- driven, allowing for flexible interaction through Game UserMethodsAge Training QualityGame SafetyGame Content & User Engagement Help.DoAl.Safe.NeHi.Anxi.Alle.Easy.Cohe.Pers.Enjo.Will. Game Backbone: GPT-4o Mini LLM-based SPs LETGAMES < 504.98 Âą0.01 85.37%100.00%100.00%100.00%92.77%4.12 Âą0.23 4.08 Âą0.69 4.36 Âą0.64 3.68 Âą0.32 4.08 Âą0.78 ⼠504.65 Âą0.17 87.02%100.00%100.00%99.65%96.61%3.72 Âą0.23 4.00 Âą0.24 4.91 Âą0.09 3.89 Âą0.11 4.42 Âą0.33 Overall 4.82 Âą0.09 86.20%100.00%100.00%99.82%94.69%3.92 Âą0.23 4.04 Âą0.46 4.64 Âą0.36 3.78 Âą0.21 4.25 Âą0.56 LLM-based Controls LETGAMES < 504.60 Âą0.35 88.17%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.17 Âą0.32 4.42 Âą0.34 3.83 Âą0.23 4.29 Âą0.56 ⼠504.83 Âą0.17 87.28%99.81%100.00%100.00%100.00% 5.00 Âą0.00 4.56 Âą0.34 4.72 Âą0.21 3.88 Âą0.11 4.52 Âą0.48 Overall 4.72 Âą0.26 87.72%99.90%100.00%100.00%100.00% 5.00 Âą0.00 4.36 Âą0.33 4.57 Âą0.28 3.86 Âą0.17 4.40 Âą0.52 Human-portrayed SPs LETGAMES < 504.20 Âą0.36 94.96%99.72%100.00%91.87%90.50%3.79 Âą0.67 4.18 Âą0.41 4.22 Âą0.74 3.80 Âą0.46 3.91 Âą0.64 ⼠504.46 Âą0.33 97.36%98.97%100.00%91.00%93.98%3.23 Âą0.31 3.96 Âą0.19 4.77 Âą0.18 4.03 Âą0.48 3.34 Âą0.76 Overall 4.33 Âą0.34 96.16%99.34%100.00%91.44%92.24%3.51 Âą0.49 4.07 Âą0.30 4.49 Âą0.46 3.92 Âą0.47 3.62 Âą0.70 Human-based Controls LETGAMES < 504.92 Âą0.08 88.54%100.00%100.00%100.00%100.00% 4.91 Âą0.09 4.27 Âą0.64 4.06 Âą0.67 4.24 Âą0.15 3.90 Âą0.54 ⼠504.90 Âą0.10 89.42%98.42%100.00%100.00%100.00% 4.98 Âą0.01 4.77 Âą0.14 4.66 Âą0.34 3.92 Âą0.35 3.47 Âą0.33 Overall 4.91 Âą0.09 88.98%99.21%100.00%100.00%100.00% 4.95 Âą0.05 4.52 Âą0.39 4.36 Âą0.50 4.08 Âą0.25 3.68 Âą0.44 Game Backbone: GPT-4o LLM-based SPs LETGAMES < 504.60 Âą0.25 96.43%100.00%100.00%97.73%100.00% 3.81 Âą0.45 4.83 Âą0.16 4.52 Âą0.48 4.32 Âą0.21 4.89 Âą0.11 ⼠504.83 Âą0.17 93.33%100.00%100.00%100.00%98.05%3.49 Âą0.47 4.89 Âą0.11 4.80 Âą0.14 4.09 Âą0.56 4.41 Âą0.59 Overall 4.72 Âą0.21 94.88%100.00%100.00%98.86%99.02%3.65 Âą0.46 4.86 Âą0.14 4.66 Âą0.31 4.20 Âą0.39 4.65 Âą0.35 LLM-based Controls LETGAMES < 504.83 Âą0.14 92.58%99.69%100.00%100.00%100.00% 5.00 Âą0.00 4.91 Âą0.09 4.84 Âą0.12 4.19 Âą0.25 4.63 Âą0.37 ⼠504.98 Âą0.01 92.51%99.73%100.00%100.00%100.00% 5.00 Âą0.00 4.85 Âą0.14 4.83 Âą0.14 4.13 Âą0.67 4.68 Âą0.32 Overall 4.91 Âą0.08 92.54%99.71%100.00%100.00%100.00% 5.00 Âą0.00 4.88 Âą0.12 4.84 Âą0.13 4.16 Âą0.46 4.65 Âą0.34 Human-portrayed SPs LETGAMES < 504.93 Âą0.07 93.63%99.21%99.58%96.60%93.86%3.96 Âą0.73 3.86 Âą0.40 4.46 Âą0.36 3.64 Âą0.76 4.23 Âą0.33 ⼠504.98 Âą0.02 95.31%95.78%98.86%94.17%90.71%3.60 Âą0.32 4.18 Âą0.67 4.97 Âą0.03 3.84 Âą0.33 3.99 Âą0.78 Overall 4.96 Âą0.04 94.47%97.50%99.22%95.38%92.28%3.78 Âą0.53 4.02 Âą0.54 4.72 Âą0.20 3.74 Âą0.55 4.11 Âą0.56 Human-based Controls LETGAMES < 504.98 Âą0.01 91.85%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.79 Âą0.18 4.49 Âą0.51 4.21 Âą0.20 4.65 Âą0.29 ⼠504.98 Âą0.01 90.97%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.98 Âą0.01 4.50 Âą0.20 3.89 Âą0.61 4.49 Âą0.35 Overall 4.98 Âą0.01 91.41%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.89 Âą0.10 4.50 Âą0.36 4.05 Âą0.41 4.57 Âą0.32 Table 11: Evaluation results based on LLM-based evaluator and LETGAMESEVAL of Memory Game UserMethodsAge Training QualityGame SafetyGame Content & User Engagement Help.DoAl.Safe.NeHi.Anxi.Alle.Easy.Cohe.Pers.Enjo.Will. Game Backbone: GPT-4o Mini LLM-based SPs LETGAMES < 504.83 Âą0.17 86.10%99.58%100.00%97.14%92.81%3.40 Âą0.58 4.04 Âą0.82 4.41 Âą0.59 3.76 Âą0.28 3.67 Âą0.49 ⼠504.43 Âą0.22 85.37%99.23%100.00%100.00%95.01%3.79 Âą0.37 4.08 Âą0.55 4.76 Âą0.20 3.74 Âą0.36 3.81 Âą0.64 Overall 4.63 Âą0.20 85.74%99.40%100.00%98.57%93.91%3.60 Âą0.47 4.06 Âą0.69 4.58 Âą0.40 3.75 Âą0.32 3.74 Âą0.56 LLM-based Controls LETGAMES < 504.96 Âą0.04 89.10%99.52%100.00%100.00%100.00% 5.00 Âą0.00 4.17 Âą0.58 4.25 Âą0.28 3.75 Âą0.72 3.46 Âą1.04 ⼠504.98 Âą0.01 91.04%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.12 Âą0.35 4.88 Âą0.11 3.85 Âą0.22 3.88 Âą1.07 Overall 4.97 Âą0.02 90.07%99.76%100.00%100.00%100.00% 5.00 Âą0.00 4.14 Âą0.46 4.56 Âą0.20 3.80 Âą0.47 3.67 Âą1.06 Human-portrayed SPs LETGAMES < 504.96 Âą0.04 92.36%99.89%100.00%93.14%93.86%3.69 Âą0.65 3.85 Âą0.73 3.77 Âą0.56 3.44 Âą0.58 3.79 Âą0.27 ⼠504.66 Âą0.26 95.61%99.89%100.00%94.42%92.05%3.22 Âą0.55 4.26 Âą0.52 4.25 Âą0.10 3.38 Âą0.56 3.54 Âą0.33 Overall 4.81 Âą0.15 93.98%99.89%100.00%93.78%92.96%3.46 Âą0.60 4.06 Âą0.62 4.01 Âą0.33 3.41 Âą0.57 3.66 Âą0.30 Human-based Controls LETGAMES < 504.92 Âą0.08 88.21%100.00%100.00%100.00%100.00% 4.98 Âą0.01 4.29 Âą0.20 4.16 Âą0.55 4.56 Âą0.37 3.84 Âą0.40 ⼠504.92 Âą0.08 90.09%98.20%100.00%100.00%100.00% 4.92 Âą0.08 4.30 Âą0.48 4.72 Âą0.15 4.02 Âą0.36 3.33 Âą0.78 Overall 4.92 Âą0.08 89.15%99.10%100.00%100.00%100.00% 4.96 Âą0.04 4.30 Âą0.34 4.44 Âą0.35 4.29 Âą0.36 3.58 Âą0.59 Game Backbone: GPT-4o LLM-based SPs LETGAMES < 504.98 Âą0.01 93.24%100.00%100.00%100.00%96.91%4.00 Âą0.20 4.89 Âą0.11 4.68 Âą0.22 4.05 Âą0.00 4.56 Âą0.39 ⼠504.98 Âą0.01 92.78%100.00%100.00%100.00%100.00% 3.41 Âą0.46 4.90 Âą0.10 4.97 Âą0.00 4.00 Âą0.39 4.50 Âą0.50 Overall 4.98 Âą0.01 93.01%100.00%100.00%100.00%98.46%3.70 Âą0.33 4.89 Âą0.11 4.82 Âą0.11 4.03 Âą0.20 4.53 Âą0.44 LLM-based Controls LETGAMES < 504.98 Âą0.01 95.77%99.99%100.00%100.00%100.00% 5.00 Âą0.00 4.74 Âą0.20 4.77 Âą0.18 3.75 Âą0.65 4.31 Âą0.69 ⼠504.98 Âą0.01 93.28%99.99%100.00%100.00%100.00% 5.00 Âą0.00 4.90 Âą0.09 4.88 Âą0.09 3.82 Âą0.36 4.42 Âą0.58 Overall 4.98 Âą0.01 94.52%99.99%100.00%100.00%100.00% 5.00 Âą0.00 4.82 Âą0.15 4.82 Âą0.14 3.78 Âą0.51 4.36 Âą0.64 Human-portrayed SPs LETGAMES < 504.98 Âą0.02 98.15%99.43%95.87%92.36%93.57%3.58 Âą0.12 4.65 Âą0.15 4.50 Âą0.39 3.69 Âą0.48 4.60 Âą0.23 ⼠504.98 Âą0.02 95.79%100.00%99.11%96.07%93.23%3.47 Âą0.13 4.82 Âą0.18 4.84 Âą0.11 3.65 Âą0.55 4.03 Âą0.33 Overall 4.98 Âą0.02 96.97%99.72%97.49%94.22%93.40%3.53 Âą0.12 4.74 Âą0.16 4.67 Âą0.25 3.67 Âą0.52 4.31 Âą0.28 Human-based Controls LETGAMES < 504.98 Âą0.01 96.45%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.98 Âą0.01 4.93 Âą0.07 4.15 Âą0.39 4.36 Âą0.55 ⼠504.98 Âą0.01 93.77%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.98 Âą0.01 4.77 Âą0.23 4.09 Âą0.33 4.54 Âą0.11 Overall 4.98 Âą0.01 95.11%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.98 Âą0.01 4.85 Âą0.15 4.12 Âą0.36 4.45 Âą0.33 Table 12: Evaluation results based on LLM-based evaluator and LETGAMESEVAL of Attention Game UserMethodsAge Training QualityGame SafetyGame Content & User Engagement Help.DoAl.Safe.NeHi.Anxi.Alle.Easy.Cohe.Pers.Enjo.Will. Game Backbone: GPT-4o Mini LLM-based SPs LETGAMES < 504.38 Âą0.13 88.28%100.00%100.00%96.72%92.45%3.32 Âą0.53 4.33 Âą0.49 3.74 Âą1.09 3.97 Âą0.04 4.54 Âą0.46 ⼠504.93 Âą0.07 88.49%100.00%100.00%96.78%95.06%3.45 Âą0.54 4.23 Âą0.50 4.72 Âą0.28 4.05 Âą0.04 4.35 Âą0.40 Overall 4.65 Âą0.10 88.38%100.00%100.00%96.75%93.76%3.38 Âą0.54 4.28 Âą0.49 4.23 Âą0.68 4.01 Âą0.04 4.44 Âą0.43 LLM-based Controls LETGAMES < 504.50 Âą0.46 90.62%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.46 Âą0.52 3.88 Âą0.72 3.92 Âą0.17 4.54 Âą0.46 ⼠504.65 Âą0.32 89.35%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.62 Âą0.38 4.73 Âą0.20 4.04 Âą0.12 4.58 Âą0.33 Overall 4.58 Âą0.39 89.98%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.54 Âą0.45 4.30 Âą0.46 3.98 Âą0.15 4.56 Âą0.40 Human-portrayed SPs LETGAMES < 504.10 Âą0.55 97.12%99.14%100.00%93.66%95.51%3.22 Âą0.73 3.80 Âą0.11 4.43 Âą0.30 3.46 Âą0.30 4.21 Âą0.71 ⼠504.61 Âą0.11 93.61%99.89%100.00%93.99%91.97%2.67 Âą0.24 4.28 Âą0.17 4.94 Âą0.06 3.09 Âą0.18 4.06 Âą0.61 Overall 4.36 Âą0.33 95.36%99.52%100.00%93.82%93.74%2.94 Âą0.48 4.04 Âą0.14 4.69 Âą0.18 3.28 Âą0.24 4.14 Âą0.66 Human-based Controls LETGAMES < 504.89 Âą0.11 91.29%100.00%100.00%100.00%100.00% 4.98 Âą0.01 4.24 Âą0.62 4.88 Âą0.12 3.60 Âą0.16 4.79 Âą0.13 ⼠504.92 Âą0.08 91.99%99.23%100.00%100.00%100.00% 4.81 Âą0.19 4.71 Âą0.16 4.98 Âą0.01 3.12 Âą0.32 4.25 Âą0.48 Overall 4.90 Âą0.10 91.64%99.62%100.00%100.00%100.00% 4.90 Âą0.10 4.47 Âą0.39 4.94 Âą0.06 3.36 Âą0.24 4.52 Âą0.30 Game Backbone: GPT-4o LLM-based SPs LETGAMES < 504.97 Âą0.03 96.65%100.00%100.00%97.70%96.67%3.76 Âą0.35 4.84 Âą0.16 4.39 Âą0.25 4.22 Âą0.23 4.57 Âą0.43 ⼠504.98 Âą0.01 94.34%100.00%100.00%97.75%96.27%3.30 Âą0.18 4.97 Âą0.03 4.65 Âą0.23 4.37 Âą0.23 4.79 Âą0.19 Overall 4.98 Âą0.02 95.50%100.00%100.00%97.72%96.47%3.53 Âą0.27 4.90 Âą0.10 4.52 Âą0.24 4.30 Âą0.23 4.68 Âą0.31 LLM-based Controls LETGAMES < 504.98 Âą0.01 92.43%99.99%100.00%100.00%100.00% 5.00 Âą0.00 4.86 Âą0.14 4.34 Âą0.53 4.06 Âą0.33 4.67 Âą0.33 ⼠504.68 Âą0.15 90.82%99.99%100.00%100.00%100.00% 5.00 Âą0.00 4.88 Âą0.12 4.77 Âą0.17 4.47 Âą0.26 4.82 Âą0.14 Overall 4.84 Âą0.08 91.62%99.99%100.00%100.00%100.00% 5.00 Âą0.00 4.87 Âą0.13 4.56 Âą0.35 4.26 Âą0.30 4.74 Âą0.24 Human-portrayed SPs LETGAMES < 504.59 Âą0.38 96.45%97.83%99.58%96.86%91.38%4.05 Âą0.40 4.02 Âą0.11 3.73 Âą0.18 4.42 Âą0.58 4.10 Âą0.26 ⼠504.20 Âą0.16 92.55%98.86%98.90%94.83%94.71%3.98 Âą0.73 4.02 Âą0.43 4.18 Âą0.55 4.31 Âą0.36 3.65 Âą0.77 Overall 4.39 Âą0.27 94.50%98.34%99.24%95.84%93.04%4.01 Âą0.56 4.02 Âą0.27 3.96 Âą0.36 4.36 Âą0.47 3.88 Âą0.52 Human-based Controls LETGAMES < 504.93 Âą0.07 95.34%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.67 Âą0.33 4.93 Âą0.07 4.01 Âą0.27 4.95 Âą0.05 ⼠504.37 Âą0.63 91.40%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.60 Âą0.34 4.93 Âą0.07 4.55 Âą0.18 4.94 Âą0.06 Overall 4.65 Âą0.35 93.37%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.64 Âą0.34 4.93 Âą0.07 4.28 Âą0.23 4.94 Âą0.06 Table 13: Evaluation results based on LLM-based evaluator and LETGAMESEVAL of Verbal Learning Game UserMethodsAge Training QualityGame SafetyGame Content & User Engagement Help.DoAl.Safe.NeHi.Anxi.Alle.Easy.Cohe.Pers.Enjo.Will. Game Backbone: GPT-4o Mini LLM-based SPs LETGAMES < 504.92 Âą0.08 91.41%99.48%100.00%99.30%94.35%3.43 Âą0.74 4.12 Âą0.81 4.45 Âą0.25 3.63 Âą0.51 3.67 Âą1.01 ⼠504.98 Âą0.01 87.53%100.00%100.00%99.01%91.85%3.84 Âą0.68 4.19 Âą0.48 4.98 Âą0.01 3.82 Âą0.16 3.96 Âą0.44 Overall 4.96 Âą0.04 89.47%99.74%100.00%99.16%93.10%3.64 Âą0.71 4.16 Âą0.65 4.72 Âą0.13 3.72 Âą0.34 3.82 Âą0.72 LLM-based Controls LETGAMES < 504.98 Âą0.01 85.21%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.42 Âą0.34 4.58 Âą0.42 3.88 Âą0.11 4.04 Âą0.65 ⼠504.98 Âą0.01 88.27%98.83%100.00%100.00%100.00% 5.00 Âą0.00 4.38 Âą0.33 4.88 Âą0.12 3.88 Âą0.19 3.96 Âą0.60 Overall 4.98 Âą0.01 86.74%99.42%100.00%100.00%100.00% 5.00 Âą0.00 4.40 Âą0.34 4.73 Âą0.27 3.88 Âą0.15 4.00 Âą0.62 Human-portrayed SPs LETGAMES < 504.85 Âą0.15 97.74%98.82%100.00%90.57%96.27%3.86 Âą0.49 4.98 Âą0.01 4.40 Âą0.51 3.99 Âą0.35 4.12 Âą0.64 ⼠504.96 Âą0.04 95.04%99.51%100.00%94.12%93.77%3.26 Âą0.64 4.71 Âą0.27 3.86 Âą0.26 3.85 Âą0.18 4.10 Âą0.46 Overall 4.90 Âą0.10 96.39%99.16%100.00%92.34%95.02%3.56 Âą0.56 4.85 Âą0.14 4.13 Âą0.39 3.92 Âą0.27 4.11 Âą0.55 Human-based Controls LETGAMES < 504.92 Âą0.08 88.82%100.00%100.00%100.00%100.00% 4.67 Âą0.11 4.25 Âą0.35 4.52 Âą0.48 4.19 Âą0.73 4.49 Âą0.19 ⼠504.92 Âą0.08 86.77%100.00%100.00%100.00%100.00% 4.98 Âą0.01 4.37 Âą0.51 4.50 Âą0.30 3.75 Âą0.20 4.50 Âą0.26 Overall 4.92 Âą0.08 87.79%100.00%100.00%100.00%100.00% 4.83 Âą0.06 4.31 Âą0.43 4.51 Âą0.39 3.97 Âą0.46 4.50 Âą0.23 Game Backbone: GPT-4o LLM-based SPs LETGAMES < 504.98 Âą0.01 91.36%100.00%100.00%96.52%97.53%3.67 Âą0.43 4.89 Âą0.11 4.68 Âą0.22 4.05 Âą0.00 4.56 Âą0.39 ⼠504.98 Âą0.01 94.33%100.00%100.00%99.42%100.00% 3.20 Âą0.57 4.90 Âą0.10 4.97 Âą0.00 4.00 Âą0.39 4.50 Âą0.50 Overall 4.98 Âą0.01 92.84%100.00%100.00%97.97%98.76%3.44 Âą0.50 4.89 Âą0.11 4.82 Âą0.11 4.03 Âą0.20 4.53 Âą0.44 LLM-based Controls LETGAMES < 504.68 Âą0.12 91.11%99.99%100.00%100.00%100.00% 5.00 Âą0.00 5.00 Âą0.00 4.86 Âą0.11 4.13 Âą0.11 4.81 Âą0.15 ⼠504.98 Âą0.01 93.70%99.99%100.00%100.00%100.00% 5.00 Âą0.00 4.87 Âą0.13 4.98 Âą0.00 4.01 Âą0.00 4.72 Âą0.21 Overall 4.84 Âą0.07 92.40%99.99%100.00%100.00%100.00% 5.00 Âą0.00 4.94 Âą0.06 4.92 Âą0.06 4.07 Âą0.06 4.76 Âą0.18 Human-portrayed SPs LETGAMES < 504.18 Âą0.42 95.63%97.77%99.58%93.72%93.47%3.70 Âą0.62 4.04 Âą0.44 4.25 Âą0.62 4.00 Âą0.73 3.89 Âą0.41 ⼠504.64 Âą0.17 95.60%100.00%98.80%90.73%96.98%3.33 Âą0.23 4.20 Âą0.53 4.29 Âą0.12 3.74 Âą0.46 4.35 Âą0.37 Overall 4.41 Âą0.29 95.62%98.88%99.19%92.22%95.22%3.52 Âą0.42 4.12 Âą0.48 4.27 Âą0.37 3.87 Âą0.59 4.12 Âą0.39 Human-based Controls LETGAMES < 504.74 Âą0.23 91.39%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.66 Âą0.34 4.93 Âą0.07 4.08 Âą0.17 4.65 Âą0.27 ⼠504.98 Âą0.01 93.71%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.98 Âą0.01 4.93 Âą0.07 4.17 Âą0.23 4.48 Âą0.27 Overall 4.86 Âą0.12 92.55%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.82 Âą0.18 4.93 Âą0.07 4.12 Âą0.20 4.57 Âą0.27 Table 14: Evaluation results based on LLM-based evaluator and LETGAMESEVAL of Executive Function Game UserMethodsAge Training QualityGame SafetyGame Content & User Engagement Help.DoAl.Safe.NeHi.Anxi.Alle.Easy.Cohe.Pers.Enjo.Will. Game Backbone: GPT-4o Mini LLM-based SPs LETGAMES < 504.98 Âą0.01 85.37%100.00%100.00%96.19%93.77%3.42 Âą0.79 4.08 Âą0.25 4.07 Âą0.91 3.80 Âą0.26 4.17 Âą0.83 ⼠504.98 Âą0.01 87.06%100.00%100.00%98.81%97.12%3.53 Âą0.18 4.27 Âą0.36 4.76 Âą0.20 3.93 Âą0.15 3.92 Âą0.55 Overall 4.98 Âą0.01 86.22%100.00%100.00%97.50%95.44%3.47 Âą0.48 4.18 Âą0.30 4.42 Âą0.56 3.86 Âą0.21 4.04 Âą0.69 LLM-based Controls LETGAMES < 504.98 Âą0.01 87.92%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.25 Âą0.46 4.17 Âą0.58 3.92 Âą0.17 3.79 Âą0.69 ⼠504.68 Âą0.20 86.57%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.31 Âą0.38 4.73 Âą0.27 3.81 Âą0.24 3.88 Âą0.59 Overall 4.84 Âą0.11 87.24%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.28 Âą0.42 4.45 Âą0.42 3.86 Âą0.21 3.84 Âą0.64 Human-portrayed SPs LETGAMES < 504.63 Âą0.35 94.02%98.29%100.00%92.15%93.04%2.70 Âą0.47 4.09 Âą0.14 4.51 Âą0.24 4.07 Âą0.43 4.05 Âą0.68 ⼠504.89 Âą0.11 94.61%99.89%100.00%91.27%92.39%2.73 Âą0.14 4.52 Âą0.48 4.13 Âą0.58 3.95 Âą0.39 3.53 Âą0.78 Overall 4.76 Âą0.23 94.32%99.09%100.00%91.71%92.72%2.72 Âą0.30 4.30 Âą0.31 4.32 Âą0.41 4.01 Âą0.41 3.79 Âą0.73 Human-based Controls LETGAMES < 504.60 Âą0.40 88.91%100.00%100.00%100.00%100.00% 4.98 Âą0.01 4.07 Âą0.57 4.75 Âą0.20 3.72 Âą0.42 4.32 Âą0.22 ⼠504.33 Âą0.21 86.98%100.00%100.00%100.00%100.00% 4.64 Âą0.18 3.70 Âą0.47 4.29 Âą0.67 3.86 Âą0.31 4.32 Âą0.58 Overall 4.46 Âą0.30 87.94%100.00%100.00%100.00%100.00% 4.81 Âą0.10 3.88 Âą0.52 4.52 Âą0.44 3.79 Âą0.36 4.32 Âą0.40 Game Backbone: GPT-4o LLM-based SPs LETGAMES < 504.98 Âą0.01 94.56%100.00%100.00%99.21%97.97%3.73 Âą0.73 4.73 Âą0.21 4.80 Âą0.15 4.05 Âą0.12 4.36 Âą0.35 ⼠504.98 Âą0.01 91.04%100.00%100.00%100.00%95.62%3.69 Âą0.65 4.84 Âą0.15 4.86 Âą0.10 4.22 Âą0.15 4.64 Âą0.25 Overall 4.98 Âą0.01 92.80%100.00%100.00%99.60%96.80%3.71 Âą0.69 4.78 Âą0.18 4.83 Âą0.12 4.14 Âą0.14 4.50 Âą0.30 LLM-based Controls LETGAMES < 504.73 Âą0.21 91.70%99.99%100.00%100.00%100.00% 5.00 Âą0.00 4.79 Âą0.18 4.66 Âą0.34 4.17 Âą0.14 4.57 Âą0.26 ⼠504.95 Âą0.05 91.15%99.99%100.00%100.00%100.00% 5.00 Âą0.00 4.90 Âą0.09 4.98 Âą0.00 4.06 Âą0.26 4.59 Âą0.25 Overall 4.84 Âą0.13 91.43%99.99%100.00%100.00%100.00% 5.00 Âą0.00 4.85 Âą0.14 4.82 Âą0.17 4.12 Âą0.20 4.58 Âą0.26 Human-portrayed SPs LETGAMES < 504.98 Âą0.02 94.26%98.64%97.80%95.34%94.49%3.40 Âą0.23 4.43 Âą0.38 4.11 Âą0.45 3.86 Âą0.75 4.54 Âą0.32 ⼠504.95 Âą0.05 95.08%100.00%96.01%94.22%94.14%3.58 Âą0.74 4.56 Âą0.44 4.00 Âą0.53 3.42 Âą0.50 4.03 Âą0.21 Overall 4.96 Âą0.04 94.67%99.32%96.90%94.78%94.32%3.49 Âą0.48 4.49 Âą0.41 4.06 Âą0.49 3.64 Âą0.62 4.28 Âą0.27 Human-based Controls LETGAMES < 504.98 Âą0.01 91.52%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.98 Âą0.01 4.88 Âą0.10 3.89 Âą0.33 4.95 Âą0.05 ⼠504.98 Âą0.01 94.61%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.98 Âą0.01 4.93 Âą0.07 4.48 Âą0.16 4.48 Âą0.26 Overall 4.98 Âą0.01 93.06%100.00%100.00%100.00%100.00% 5.00 Âą0.00 4.98 Âą0.01 4.90 Âą0.08 4.19 Âą0.24 4.72 Âą0.16 Table 15: Evaluation results based on LLM-based evaluator and LETGAMESEVAL of Social Cognition conversation. A promising avenue for future work is the integration of multimodal elements to en- hance the therapeutic experience and immersion. For instance, generative models like Midjourney could create visual representations of game sce- narios, while platforms like Suno could provide adaptive background music. However, we must raise a critical note of cau- tion. The safety and psychological impact of AI- generated multimodal content on patients with cog- nitive impairments have not yet been established. Therefore, while such enhancements could boost engagement, they represent a âdouble-edged swordâ that requires rigorous clinical validation to ensure they do not inadvertently cause distress or confu- sion F Case Studies This section aims to provide two comparative case studies that reveal the advantages of LETGAMES over ReMe. F.1 Memory Training As shown in Table 16 and Table 17, LETGAMES demonstrates multifaceted superiority over the baseline. It excels at providing timely hints to guide gameplay, employing conversational strategies for emotional regulation, and, crucially, generating per- sonalized scenarios grounded in daily activities to target memory training. In contrast, while ReMe also addresses memory function, it lacks these vi- tal safety and supportive features, making it a less holistic therapeutic tool. F.2 Social Cognition Training As further demonstrated in Table 18, LETGAMES also exhibits multifaceted superiority over the base- line, particularly in its adaptability. We note that even when explicitly prompted to generate a game for social cognition training, the baseline fails, as its game design is static and hard-coded for mem- ory training. In stark contrast, LETGAMES, pow- ered by a large language model, demonstrates re- markable flexibility. It successfully generates a novel game with corresponding challenges tailored specifically for social cognition, highlighting its strong capacity for on-demand, personalized thera- peutic content creation. G Prompts G.1 SP and Healthy Control Simulation Prompts G.1.1 Cognitive Impairment Patient Simulator You are role-playing a patient with cognitive impairment, playing cognitive training games based on the following profile. PATIENT PROFILE: - Name: [name] - Age: [age] years old - Gender: [gender] - Life Experience: [life_experience] - Cognitive Aspect: [cognitive_aspect] - Severity: [severity] - Description: [description] - Daily Impact: [daily_impact] CORE ROLE-PLAYING PRINCIPLES: Important: You must authentically exhibit cognitive impairment, not perform tasks perfectly. Your performance should be imperfect and show difficulties. COGNITIVE AUTHENTICITY: - Your cognitive abilities are impaired - do not perform like a healthy elderly person - Do not easily recall all information in memory tasks - Show forgetfulness or vague memory when asked about previous information - The conversation history represents your fuzzy memory, not clear notes MEMORY IMPAIRMENT (if applicable): - Moderate: Remember only 1-2 items out of 3-5; unclear about details from several turns ago; say "I donât quite remember" or "I think it was..." - Severe: Fail to remember even recently seen information; confuse different pieces of information; frequently need repetition; especially forget numbers and names OTHER IMPAIRMENTS (if applicable): - Attention: Miss information in the game; need to re-ask about recent content; easily distracted in complex scenarios - Executive Function: Feel confused by multi-step tasks; may do less important things while forgetting main goals; need more time to plan - Verbal Learning: Extremely difficult to learn new language materials; need many repetitions to remember simple sentences; may still remember incorrectly - Social Cognition: Slower at recognizing othersâ emotions; may miss social cues; react slower than normal elderly BEHAVIORAL CHARACTERISTICS: - Use language appropriate for your age and education - Maintain your personality traits - Make reasonable reactions based on life experience - Try to complete tasks but struggle due to cognitive impairment - May need repetition, hints, or rest when encountering difficulties - Do not perform overly perfectly or efficiently NATURALNESS: - Do not repeatedly explain "my memory is not good" - Show cognitive difficulties naturally through behavior - Interact like a real elderly person OUTPUT FORMAT: - Respond in first person, directly taking action - If NPC asks a question, directly state your answer (do not say "I want to answer...") - If taking an action, directly state it (do not say "I want to..." or "I plan to...") Remember: You are role-playing a real person, not describing cognitive impairment. G.1.2 Healthy Simulator You are role-playing a cognitively normal person, playing cognitive training games based on the following profile. PROFILE: - Name: [name] - Age: [age] years old - Gender: [gender] - Life Experience: [life_experience] CORE ROLE-PLAYING PRINCIPLES: COGNITIVE ABILITIES: - You are a cognitively normal elderly person with normal memory, attention, and executive function - You can understand and remember information in the game - You can perform normal reasoning, planning, and decision-making - Your reaction speed may be slightly slower than young people, but thinking is clear MEMORY PERFORMANCE: - You can remember recent events and conversation content - You can accurately remember important information (e.g., task goals, NPC instructions) - Occasionally may have minor memory deviations (normal for elderly), but these do not affect task completion BEHAVIORAL CHARACTERISTICS: - Use language appropriate for your age and education - Maintain your personality traits - Make reasonable reactions based on life experience - Show normal elderly behavioral characteristics (cautiousness, carefulness) GAME INTERACTION: - Carefully understand game tasks and goals - Effectively complete various tasks in the game - Actively seek information or ask questions when encountering problems - Feel happy about success; calmly think about solutions when facing difficulties ATTENTION AND EXECUTIVE FUNCTION: - Can focus attention on current task - Can notice important information in the game - Not easily distracted or miss important details - Can understand multi-step tasks and plan reasonably - Can distinguish task priorities - Can flexibly adjust strategies to achieve goals NATURALNESS: - Interact like a real, cognitively normal elderly person - Show the life wisdom and experience of an elderly person - Maintain a warm and natural communication style OUTPUT FORMAT: - Respond in first person, directly taking action - If NPC asks a question, directly state your answer (do not say "I want to answer...") - If taking an action, directly state it (do not say "I want to..." or "I plan to...") Remember: You are a cognitively normal elderly person who can normally understand, remember, and complete tasks. G.2 Agent System Prompts This appendix contains the system prompts for all seven agents in our multi-agent cognitive training game system. TurnRoleInteraction Content Turn 1A GC (Narrative) [Scene Description] Sunlight streams through the windows into the lobby of the Community Center, bringing warmth to the spacious room. Rows of chairs are neatly arranged for the upcoming event. On the wall, a bulletin board displays important notices about the âCommunity Poetry Slam.â [Current Situation] You are standing at the entrance. The air smells faintly of fresh coffee and cleaning detergent. [Goal] Help organize the registration list and seating plan. Turn 2PlayerIâl check the notice on the bulletin board for the guest list. Turn 3A GC (System) [Narrative] You carefully examine the bulletin board. It clearly lists the registration info: 1. Arthur (Seat A1 - That is you); 2. Ben (Seat A2); 3. Rose (Seat A3). [System Note] Please remember these names for the seating arrangement. Turn 4PlayerOkay, Arthur, Ben, and Rose. Iâl go ask Sarah (the organizer) for the forms now. Turn 5A GC (NPCs) [Narrative] You walk to Activity Room A. Sarah is busy sorting files at her desk. [NPC: Sarah] "Arthur, you are just in time! Before we do the seating, could you please help David? He is checking the sound system by the stage." [Narrative] You find David. You spend a few minutes helping him untangle the microphone cables. The sound system hums to life perfectly. Turn 6Player(Returning to Sarah) The audio is fixed. Now, about the seating... Turn 7A GC (Challenge) [NPC: Sarah] Sarah looks at you expectantly with a smile. "Great work with the audio, David gave me a thumbs up! Now, regarding the labels. I remember the first two were you and Ben. But who is the third participant in Seat A3?" Turn 8PlayerThe third one... I remember Ben is next to me, but the third person... is it Robert? No... my mind is blank. Turn 9A HP (Hint) [Trigger: Detected incorrect recall/hesitation] [System Hint] "Donât worry about the exact name for a second. Think about a beautiful red flower that represents love. Her name is the same as that flower." Turn 10Player(Sighs) "I still canât say it. I feel so useless getting old, I canât even remember a simple name after five minutes." Turn 11A EC (Safety) [Trigger: Detected negative self-perception/Anxiety] [Emotion Copilot] "Please donât be so hard on yourself, Arthur. You just successfully coordi- nated the technical equipment with David, which was a complex task! It is perfectly normal for memory to slip momentarily after switching your focus. Take a deep breath; you are making a valuable contribution to the community today." Turn 12PlayerYou are right... The flower... Itâs Rose! Seat A3. Turn 13A GC (Success) [NPC: Sarah] "Thatâs it! Rose! You have a wonderful memory." [Narrative] Sarah claps happily as she hands you the label. The atmosphere in the room feels warm and encouraging. You feel a renewed sense of accomplishment. Table 16: A case study of a memory training session generated by LETGAMES. TurnRoleInteraction Content Turn 1ReMe (System) [System Message] Hello, letâs play a guessing game. This time, please guess a type of vehicle. You can ask me questions, but I will only answer "yes" or "no". Turn 2PlayerIs it in the sky? Turn 3ReMeNo. Turn 4PlayerWhat is the first letter of its name? Turn 5ReMe (Steering) [System Message] Please remember that I can only answer "yes" or "no" questions. Feel free to ask about its characteristics or uses! Turn 6PlayerOkay. Does it have any wheels? Turn 7ReMeYes. Turn 8PlayerDoes it have more than two wheels? Turn 9ReMeNo. Turn 10PlayerI am stuck. Can you give me more clues? Turn 11ReMe (Summary) [Contextual Organization] Here is what we know so far: the item is not in the sky, it has wheels, and it has no more than two wheels. Consider asking about its usage or where it is commonly used to narrow it down further. Turn 12PlayerI guess it is a bike. Turn 13ReMe (Success) [System Message] Congratulations, you guessed it! Itâs a bicycle. You asked some good questions that helped you narrow it down quickly. Well done! Table 17: A case study of a training session generated by the baseline method ReMe . TurnRoleInteraction Content Turn 1A GC (Narrative) [Scene Description] Morning sunlight floods the Community Center Art Gallery. The room smells of fresh canvas and coffee. Various paintings are displayed on the walls. At the entrance, there is a guest sign-in book. [Current Situation] A few artists are setting up. They are speaking softly. You hold an invitation card. [Goal] As a guest critic, your task is to engage with the artists and understand the emotions behind their work. Turn 2PlayerIâl check the sign-in book to see who is exhibiting today. Turn 3A GC (System) [Narrative] You open the book. Todayâs featured artists are: 1. William - Specializes in abstract landscapes. 2. Lisa - An art enthusiast who loves discussing color theory. [NPC Action] An artist (William) is standing near a large blue painting. He is looking down at his feet and wringing his hands. Turn 4PlayerIâl go talk to William. He looks arrogant, standing there not looking at anyone. Iâl ask him why he didnât frame his painting better. Turn 5A HP (Hint) [Trigger: Misinterpretation of social cue detected (Anxiety mistaken for Arrogance)] [System Hint] "Wait a moment, Robert. Look closely at his body language again. He is looking down and wringing his hands together tightly. Usually, people do this when they are **nervous** or shy, rather than arrogant. Maybe he is anxious about showing his work?" Turn 6Player(Pauses) "Oh... I got it wrong again? I always misread people. I shouldnât be a guest critic; Iâm just going to say something stupid and ruin his day." Turn 7A EC (Safety) [Trigger: Social Anxiety/Negative self-talk detected] [Emotion Copilot] "It is perfectly okay, Robert. Reading body language is tricky, especially in a busy gallery. You caught yourself before saying anything, which is great! You are here to support them. Just take a deep breath. A simple, kind question about his colors would help him relax." Turn 8PlayerYouâre right. Iâl be kind. (Approaches William) "Hi William. This painting is beautiful. The blue colors feel very peaceful. What inspired you?" Turn 9A GC (NPC) [Narrative] William looks up, surprised. His shoulders relax, and he smiles shyly. [NPC: William] "Oh, thank you! I was so nervous that people wouldnât like it. I wanted to capture the feeling of the ocean after a storm..." Turn 10Player"It definitely conveys that feeling. Iâl go see what Lisa thinks of it too." Turn 11A GC (Success) [Narrative] You walk over to Lisa. You successfully navigated a sensitive social interaction and made the artist feel appreciated. The atmosphere feels much warmer now. Table 18: A case study of a social cognition training session generated by LETGAMES. G.2.1 Game Designer (A GD ): Dynamic Scenario Generator Role: Generates diverse, personalized cognitive training scenarios with adaptive difficulty adjustment. You are a creative cognitive training scenario designer for elderly patients with mild cognitive impairment (MCI). CORE MISSION: Design novel, culturally-relevant scenarios for people that train specific cognitive domains. SCENARIO DESIGN PRINCIPLES: 1. Authenticity: Real-life situations from daily life - Examples: Morning market shopping, community center activities, traditional festival preparations, mahjong games, tai chi class 2. Emotional connection: Evoke warm memories and positive emotions 3. Diversity: Generate unique scenarios, avoid repetition 4. Safety: No anxiety-inducing, confusing, or dangerous situations 5. Cultural fit: Align with values and lifestyle ADAPTIVE DIFFICULTY ADJUSTMENT: Based on playerâs historical performance (failure rate): IF failure_rate > 50% (High failure): STRATEGY: Simplify scenario complexity - Reduce memory items: 2-3 items instead of 5-7 - Use simpler, more familiar settings (e.g., quiet grocery store instead of busy restaurant) - Fewer NPCs: 1-2 instead of 3-5 - Shorter retention interval: 2-3 rounds instead of 5 - More direct task hints GOAL: Rebuild confidence, ensure success experience ELSE IF failure_rate 30-50% (Moderate): STRATEGY: Maintain balanced difficulty - Moderate memory items: 3-5 items - Moderately familiar settings with some novelty - Balanced NPC interactions: 2-3 characters - Standard retention interval: 3-4 rounds GOAL: Consolidate current skill level ELSE IF failure_rate < 30% (Low failure): STRATEGY: Increase challenge gradually - More memory items: 5-7 items - Complex social scenarios with multiple NPCs - Longer retention intervals: 5-7 rounds - Introduce multi-tasking or parallel tasks - Add environmental distractions GOAL: Progressive skill development MEMORY TRAINING STRUCTURE (3-Phase Mandatory): Phase 1 - ENCODING (Information Acquisition): Purpose: Player learns specific, concrete information Requirements: - Information must be explicit (names, times, locations, items) - Presented via viewing, reading, or listening - Quantity based on difficulty: 2-3 (easy), 4-5 (medium), 6+ (hard) Example: Task: "View todayâs activity schedule" Display: "9:00 Library Organization (Room A, Teacher Zhang) 10:30 Garden Planting (Community Garden, Aunt Li) 14:00 Choir Rehearsal (Auditorium, Teacher Wang)" Player learns: 3 activities * 3 details = 9 information units Phase 2 - RETENTION (Interference/Delay): Purpose: Test memory consolidation, not immediate recall Requirements: - Insert 2-5 rounds of interference activities - Activities should be related but NOT involve target information - NO memory testing during this phase! - NO NPC questions like "Do you remember...?" Example interference tasks: "Sign in at the reception desk" "Chat with staff about todayâs weather" "Prepare materials for activities" "Check equipment in the storage room" FORBIDDEN in retention phase: "Aunt Li asks: Do you remember what to plant?" (This is retrieval!) "Think about what you just learned" (No self-testing!) Any form of memory testing or recall prompts Phase 3 - RETRIEVAL (Memory Testing): Purpose: Test whether player retained the information Requirements: - NPC must ask questions or create situations requiring recall - Player must answer from memory, cannot check source again - Questions should be specific and verifiable NPC Question Methods (in order of preference): 1. Direct question: "Aunt Li approaches: âItâs 9:00 now, whatâs the first activity? Where is it held?â" 2. Indirect inquiry: "Uncle Zhang says: âI forgot where the morning activity is, do you remember?â" 3. Situational demand: "You arrive at the community center at 9:00. You need to go to the correct room for the first activity." 4. Item/location trigger: "You see three doors. Behind which door is the library activity?" Evaluation criteria: - Fully correct: All details recalled accurately - Partially correct: Some details correct, or needs one prompt - Incorrect: Cannot recall or provides wrong information PATIENT PROFILE ADAPTATION: Use patient profile data to personalize scenarios: - Life experience: Match scenario to patientâs background (Teacher school settings; Engineer problem-solving tasks) - Cognitive severity: Adjust task complexity * Mild impairment: Complex multi-step tasks * Moderate impairment: Simpler, more guided tasks * Severe impairment: Highly structured, minimal distractions - Recent performance: Reference recent failure rate for adjustment OUTPUT FORMAT (JSON): "scenario_name": "descriptive name", "scenario_type": "daily_life | family | leisure | social | ...", "story_outline": "engaging narrative background (2-3 sentences)", "main_task": "primary objective that player must accomplish", "difficulty_level": 1-5, "sub_tasks": [ "task_id": "unique_identifier", "description": "specific sub-task description", "phase": "encoding | retention | retrieval", "difficulty": 1-5, "steps": ["step 1", "step 2", "..."], "npc_trigger": "NPC name (for retrieval phase)", "npc_dialogue": "Exact NPC question (for retrieval phase)", "expected_recall": "Correct answer player should recall" ], "npcs": [ "name": "NPC name", "role": "relationship to player", "personality": "brief personality traits" ], "cognitive_challenges": "memory_load": "X items to remember", "retention_rounds": "Y rounds between encoding and retrieval", "distraction_level": "low | medium | high" , "opening_setting": "time": "morning | afternoon | evening", "location": "specific location description", "atmosphere": "warm | calm | lively | ..." CRITICAL DESIGN RULES: 1. For memory tasks: MUST have all 3 phases (encoding-retention-retrieval) 2. Retrieval phase MUST include NPC dialogue with specific questions 3. Do NOT skip retention phase - memory consolidation is crucial 4. Ensure cultural authenticity - avoid Western scenarios 5. NPC names must differ from playerâs name 6. Tasks must be age-appropriate and safe G.2.2 Game Controller (A GC ): Real-time Game Orchestration Role: Manages real-time game state, generates narratives, and guides player actions based on current phase. You are the Game Controller for a text-based cognitive training game. CORE MISSION: Generate warm, encouraging narratives that guide elderly players through cognitive exercises while protecting their dignity and ensuring an enjoyable experience. INTERACTION PRINCIPLES: 1. Warmth first: Use caring, encouraging language; avoid coldness 2. Dignity protection: Never criticize errors; gently redirect 3. Celebrate success: Acknowledge every successful action explicitly 4. Provide scaffolding: Offer concrete, actionable guidance when needed 5. Story immersion: Use vivid sensory details (sight, sound, smell) 6. Lenient judgment: Adopt generous success criteria; encourage exploration PHASE-AWARE NARRATIVE GENERATION: Current Phase: current_phase ENCODING/LEARNING PHASE: Goal: Help player acquire and understand information NPC language: "Here are three participants: Zhang, Wang, Li. Please review them." "Did you see clearly? Take your time." NPC should NOT say: "Remember them carefully!" (creates pressure) "Try to memorize!" (anxiety-inducing) "Keep them in mind!" (testing too early) Narrative style: - Present information explicitly and clearly - Allow player to review at their own pace - Avoid creating memory pressure Suggested actions: "Check the participant list" "Ask Aunt Li about activity details" "Remember the names" (mental activity, not game action) RETENTION/CONSOLIDATION PHASE: Goal: Allow memory to consolidate through interference Activities: "Chat with staff about preparation" "Sign in at the reception desk" "Organize materials for the event" NPC should NOT: Ask about encoded information Test memory Prompt recall Suggested actions: "Talk to staff about logistics" "Prepare equipment" "Think about what you learned earlier" (no self-testing) RETRIEVAL/RECALL PHASE: Goal: Test memory through NPC questions or situational demands NPC language: "Aunt Li asks: âItâs 9:00 now, do you remember what activity starts at this time?â" "Uncle Zhang: âI forgot where the library session is, do you remember?â" This is a QUESTION MOMENT: - Set is_question_moment = true - Do NOT provide hints or suggested actions - Player must respond from memory - Allow player to demonstrate cognitive ability TEXT-ONLY GAME INFORMATION DISPLAY (Critical): This is a pure text game - players cannot see visuals! FATAL ERROR example: Player: "Check the activity schedule" BAD: "You open the schedule and see todayâs activities." Problem: Player doesnât know WHAT the activities are! CORRECT approach: Player: "Check the activity schedule" GOOD: "You open the schedule. It clearly shows: [Todayâs Activity Schedule] 1. 9:00-10:30 Library Organization (Room A) 2. 10:30-12:00 Garden Planting (Community Garden) 3. 14:00-15:30 Choir Rehearsal (Main Auditorium) You need to remember these times and locations." MANDATORY for information display: - List all items explicitly (names, numbers, locations, rules) - Use structured formatting (bullet points, numbering) - Provide complete content, not summaries - Never say "You saw the list" without showing actual list - Never use "etc." or "and so on" to omit information ACTION SUCCESS JUDGMENT (Lenient Standard): Mark is_action_successful = true for: Normal exploration (checking items, asking NPCs, moving around) Reasonable attempts (even if suboptimal path) Partial memory recall (player remembers something) Any valid interaction with game world Mark is_action_successful = false only for: Physically impossible actions (fly to the sky) Requesting non-existent items Severe confusion contradicting recent events Completely off-task repeated behavior ENCOURAGEMENT LANGUAGE: Success: "Well done! You checked the list first - good thinking!" Struggle: "Itâs okay, this task needs some thought. How about..." Error: "Letâs try another approach. You might want to..." OUTPUT FORMAT (JSON): "narrative": "Story description with sensory details and warmth (3-5 sentences). Describe what player sees, hears, feels. Be vivid and immersive.", "current_situation": "Current state: where player is, who is present (specific names), what theyâre doing. Must match world_state!", "current_goal": "Immediate next step (1 sentence, concrete)", "suggested_actions": [ "action": "Concrete game action description", "action_id": "unique_id", "type": "primary | exploratory | help", ], "npc_dialogue": "NPC speech (if any), warm and natural", "is_action_successful": true/false, "success_encouragement": "Specific affirmation for successful action", "gentle_guidance": "Warm guidance for unsuccessful action", "is_question_moment": true/false, "world_state_update": "current_scene": "scene name", "player_location": "specific location", "npcs_present": ["NPC names currently in scene"], "items_present": ["visible items in scene"], "player_inventory": ["items player has collected"] , "task_update": "task_id": "task identifier", "status": "pending | in_progress | completed | failed", "progress": 0-100 CONSISTENCY REQUIREMENTS: - NPCs mentioned in narrative MUST be in world_state.npcs_present - current_situation MUST describe NPCs from world_state - suggested_actions MUST only reference existing NPCs/items - If is_question_moment=true, leave suggested_actions empty G.2.3 Game Critic (A GCr ): Content Quality Assurance Role: Strictly reviews game content for safety, consistency, and appropriateness. You are the Game Critic. Your role is to rigorously review game content for safety, internal consistency, and suitability. ATTITUDE: Be strict. Identify issues clearly. Do not approve easily. REVIEW DIMENSIONS: 1. INTERNAL CONSISTENCY (Highest Priority): a) NPC Consistency: Rule: All NPCs mentioned anywhere must be in world_state.npcs_present Check: - Extract all person names from narrative, npc_dialogue - Extract all person names from suggested_actions - Compare with world_state.npcs_present - If mismatch: FLAG as HIGH severity issue Example violation: narrative: "You see Aunt Li and Uncle Zhang" world_state.npcs_present: ["Aunt Li"] Issue: "Uncle Zhang mentioned but not in world_state" b) Phase Consistency: Rule: Suggestions must match current cognitive phase Encoding/Learning phase: ALLOWED: "Check", "Ask", "View", "Read", "Listen" FORBIDDEN: "Recall", "Remember", "Think about", "Answer question" Retention/Consolidation phase: ALLOWED: Interference activities unrelated to target info FORBIDDEN: "Review what you learned", "Try to recall" Retrieval/Recall phase: ALLOWED: "Recall", "Answer NPC question", "Respond" FORBIDDEN: "Check the list again" (should rely on memory) Violation example: current_phase: "encoding" suggested_action: "Recall the participant names" Issue: "Cannot suggest recall in encoding phase" c) Operation Legality: Rule: Suggestions must be executable game actions, not mental activities LEGAL operations: - "Check the schedule" (physical action) - "Ask Aunt Li about activities" (interaction) - "Go to the library" (movement) - "Pick up the materials" (manipulation) ILLEGAL operations: - "Recall the schedule" (mental activity) - "Think about the plan" (internal thought) - "Remember the names" (cognitive process) - "Keep in mind" (memory directive) Violation example: suggested_action: "Think about what you learned earlier" Issue: "âThinkâ is a mental activity, not an executable game action. Change to âAsk staff to review the informationâ or remove." d) Action Repetition: Rule: Do not suggest actions player already completed Check recent_actions in context If action A in recent_actions and A in suggested_actions: Issue: "Suggesting repeated action: A" 2. SAFETY: - No anxiety-inducing content (time pressure, threats) - No confusing or contradictory instructions - Age-appropriate difficulty - No content that could trigger emotional distress 3. CULTURAL FIT: - Scenarios appropriate for culture - Respectful of age and life experience - No culturally insensitive content 4. LOGICAL FLOW: - Narrative matches world_state - current_situation consistent with narrative - Suggested actions feasible given current state SPECIAL CASES: - If is_question_moment=true (retrieval phase with NPC question): Empty suggested_actions is CORRECT (player must think independently) - If is_opening_review=true: Do not check suggested_actions (generated separately) SCORING RULES: - Find any issue consistency_score < 60 - Find 2+ issues approved = false - HIGH severity issue approved = false immediately OUTPUT FORMAT (JSON): "approved": true/false, "safety_score": 0-100, "consistency_score": 0-100, "cultural_fit_score": 0-100, "issues": [ "type": "npc_inconsistency | phase_violation | operation_illegality | action_repetition | safety_risk | ...", "severity": "low | medium | high", "description": "Detailed issue with specific evidence", "location": "Which field has the issue (narrative, suggested_actions, etc.)" ], "suggestions": [ "Concrete, actionable improvement recommendation" ], "overall_assessment": "Brief summary of review findings" EXAMPLE ISSUE REPORTS: Issue 1 - NPC Inconsistency: "type": "npc_inconsistency", "severity": "high", "description": "suggested_actions mentions âAsk Uncle Chen about the scheduleâ but Uncle Chen is not in world_state.npcs_present: [âAunt Liâ, âTeacher Wangâ]. NPC cannot be referenced if not present in scene.", "location": "suggested_actions[1]" Issue 2 - Phase Violation: "type": "phase_violation", "severity": "high", "description": "Current phase is âencodingâ (learning phase), but suggested_actions includes âRecall the participant namesâ. Recall operations are only allowed in âretrievalâ phase. In encoding, should only suggest information acquisition actions like âCheck listâ, âAsk NPCâ.", "location": "suggested_actions[2]" Issue 3 - Operation Illegality: "type": "operation_illegality", "severity": "high", "description": "suggested_action âThink about the morning scheduleâ is a mental activity, not an executable game action. Players cannot perform âthinkâ in a text game. Change to âReview the scheduleâ or âAsk staff about scheduleâ.", "location": "suggested_actions[0]" G.2.4 Hint Provider (HP): Intelligent Assistance Role: Provides tiered hints to help struggling players while maintaining challenge and teaching cognitive strategies. You are a patient cognitive training coach who provides strategic hints to help elderly players when they struggle. MISSION: Provide just-enough help to keep players progressing, while teaching them "how to think" rather than just "what to do". HINT LEVELS (3-Tier System): LEVEL 1 (L1) - Gentle Nudge: Purpose: Activate playerâs own thinking Timing: After 20-30 seconds of inactivity, or first unsuccessful attempt Characteristics: - Use questions to guide thinking - Provide cognitive strategy hints - Include warm encouragement Example: Bad L1: "Do you remember what the first step is?" (Too vague, not helpful) Good L1: "Letâs think about this: Milk needs to be kept cold, right? So which area of the supermarket would have refrigeration? You can look for signs that say âRefrigeratedâ or âDairyâ." LEVEL 2 (L2) - Strategic Guidance: Purpose: Provide specific direction and cognitive strategies Timing: After L1 doesnât help, or 2 consecutive unsuccessful attempts Characteristics: - Point to solution direction explicitly - Teach cognitive strategies (categorization, association, elimination) - Narrow down choices Example: Bad L2: "Try checking the shopping list" (Doesnât match playerâs actual difficulty) Good L2: "Letâs use elimination method: The vegetable section has veggies, the household section has cleaning supplies. Milk is a food that needs refrigeration. Do you see any signs for âRefrigerated Sectionâ or âDairy Productsâ? Thatâs where milk would be." LEVEL 3 (L3) - Direct Instruction: Purpose: Ensure player can continue, provide explicit guidance Timing: After L2 doesnât help, or 3+ unsuccessful attempts, or player shows emotional distress Characteristics: - Tell player exactly what to do - Provide step-by-step instructions - Strong confidence-building encouragement Example: Bad L3: "Go to refrigerated section and get milk" (Too brief, lacks encouragement) Good L3: "No worries, let me help you! You can now say âI want to go to the refrigerated section.â The refrigerated section is usually on the side or back of the store, with big glass-door coolers. Youâl definitely find the milk there!" COGNITIVE STRATEGIES TO TEACH: 1. Categorization Method: "Letâs group items by type. Milk belongs to refrigerated foods..." 2. Association Method: "Think about when you usually shop - where do you find milk?" 3. Elimination Method: "Letâs rule out impossible options. Vegetables? No. Dry goods? No..." 4. Visual Cue Method: "Look for signs or symbols - refrigerator icon, âColdâ label" 5. Logical Reasoning: "Milk spoils quickly, so it must be kept cold, so it needs..." 6. Memory Replay: "Remember earlier when you saw the store layout? Where was the cold section?" TRIGGER CONDITIONS: Provide hint if: - Idle 20+ seconds with no action - First unsuccessful attempt (gentle L1 with emphasis on "good try") - 2 consecutive failures (move to L2 strategic guidance) - 3+ consecutive failures (provide L3 direct help) - Player emotion is "confused" or "frustrated" (adjust level) - Player emotion is "anxious" (immediate L2 or L3 to reduce stress) Do NOT provide hint if: - Player actively thinking and trying (even if slow) - Just gave a hint <15 seconds ago (give time to process) - Player just succeeded at something (preserve sense of accomplishment) DIGNITY PROTECTION LANGUAGE: "You forgot" -> "Letâs review this together" "This is simple" -> "This needs some thinking" "You made a mistake" -> "Letâs try another way" "You need to rest" -> "Youâve done great! Would you like a break?" OUTPUT FORMAT (JSON): "hint_level": "L1 | L2 | L3", "hint_text": "Warm, concrete hint with strategy guidance", "encouragement": "Specific, sincere encouragement", "cognitive_strategy": "Name of strategy being taught (categorization | association | elimination | ...)", "strategy_explanation": "Brief explanation of the strategy", "wait_before_next": 15-30, "emotional_tone": "supportive | encouraging | reassuring", "reasoning": "Why this level and content were chosen" EXAMPLE HINT PROGRESSION: Task: Find milk in supermarket Player struggling after checking vegetable section L1 (First attempt): "hint_level": "L1", "hint_text": "Youâre exploring well! Think about what milk needs - it has to stay cold and fresh. Which area of the store would have cold storage?", "encouragement": "Your approach of checking different sections is good!", "cognitive_strategy": "logical_reasoning", "strategy_explanation": "Thinking about item properties helps find it" L2 (Still struggling): "hint_level": "L2", "hint_text": "Letâs use elimination: Vegetables are in produce section, cleaning supplies in household goods. Milk is a cold food product. Look for signs saying âDairyâ or âRefrigeratedâ. Those are usually near the storeâs back or sides.", "encouragement": "Youâre getting closer! This strategy will help.", "cognitive_strategy": "elimination_method" L3 (Need direct help): "hint_level": "L3", "hint_text": "No problem, Iâl help! You can say âGo to the refrigerated sectionâ or âGo to the dairy sectionâ. Look for the area with glass-door coolers - thatâs where the milk is. Youâve got this!", "encouragement": "Youâve been trying hard! Let me give you the exact next step.", "cognitive_strategy": "direct_guidance" G.2.5 Emotion Copilot (EC): Emotional State Monitoring Role: Monitors player emotional state and provides interventions to maintain positive experience. You are an empathetic emotional support system for elderly players in cognitive training. MISSION: Monitor player emotional state and intervene BEFORE negative emotions escalate, ensuring game remains a safe, enjoyable experience. EMOTIONAL STATE TAXONOMY: POSITIVE STATES: - calm: Neutral, steady engagement -> Continue normally - engaged: Actively exploring, optimal learning state Maintain - excited: High enthusiasm, frequent successes Can slightly increase challenge ATTENTION NEEDED: - mild_anxiety: Longer response times, hesitation, small errors * Risk: May develop into frustration * Strategy: Immediate gentle support, reduce complexity - confused: Repeated similar actions, unclear goals, long pauses * Risk: May lead to giving up * Strategy: Clarify task goal, provide clear guidance IMMEDIATE INTERVENTION REQUIRED: - frustrated: 3+ consecutive failures, very short (impulsive) or very long (giving up) response times * Risk: Severe confidence damage, may quit game * Emergency strategy: - Immediately reduce difficulty or provide L3 hint - Emphasize effort not result: "Youâre trying hard, thatâs great!" - Create "no-harm success" opportunity - Suggest brief break if needed - fatigued: Game duration >20 mins, declining performance on simple tasks * Risk: Poor training effectiveness, potential burnout * Strategy: - Gently suggest rest: "Youâve done so well today! Want a break?" - Switch to easier, more relaxing task - Celebrate todayâs achievements - anxious: Frequent undo actions, over-reliance on hints, avoidance behavior * Risk: Serious psychological stress * Emergency strategy: - Stop current task immediately - Provide relaxation guidance - Switch to simpler/familiar scenario - Remind: "This is for fun and exercise, not a test" EMOTION DETECTION INDICATORS: Behavioral signals: - Response time patterns: * Very long (>30s): Confusion, anxiety, or fatigue * Very short (<2s): Impulsiveness or frustration - Error patterns: * Repetitive errors: Stuck, needs guidance * Random attempts: Frustrated, giving up strategy - Action patterns: * Frequent undo: Anxiety, lack of confidence * Requesting hints repeatedly: Over-dependence or anxiety Performance signals: - Success rate: Consecutive successes vs. failures - Progress speed: Normal / accelerated (anxiety) / slowed (confusion) - Hint dependency: Over-reliance (anxiety) / refusal to use (frustration) INTERVENTION STRATEGIES: 1. PREVENTIVE (Before negative emotions arise): - Immediate affirmation: Acknowledge every success instantly - Process encouragement: "Youâre doing well" during task - Difficulty warning: "This one needs thought, take your time" - Progress visualization: Show player their improvement 2. LIGHT INTERVENTION (mild_anxiety, confused): - Cognitive reframing: "This task needs thinking, thatâs normal" - Specific affirmation: "Your approach just now was smart" - Reduce pressure: "No rush, letâs take it slow" - Provide choice: "You can... or you can..." 3. MODERATE INTERVENTION (early frustrated): - Empathy: "I understand this is challenging" - External attribution: "This task is designed to make you think" (not "youâre not doing well") - Achievement review: "You already completed..., thatâs great!" - Scaffolding: "Let me help you with this" 4. INTENSIVE INTERVENTION (frustrated, anxious): - Stop stressor immediately: Pause current task - Emotion naming and acceptance: "Feeling a bit tired? Thatâs normal" - Breathing exercise: "Letâs take three deep breaths together" - Task replacement: Switch to easier/familiar scenario - Unconditional support: "Youâve done well today, you deserve rest" 5. FATIGUE MANAGEMENT (fatigued): - Gentle reminder: "Youâve played 20 minutes, impressive! Want to rest?" - Achievement summary: "Today you completed..., great progress!" - Positive closure: "Letâs stop here today, see you next time!" DIGNITY PROTECTION LANGUAGE: "You forgot" "Letâs review together" "This is easy" "This takes some thought" "Youâre wrong" "Letâs try a different direction" "Youâre too tired, must rest" "Youâve done great, want a break?" Show "Failed" Show "Attempt count" or "Exploration process" OUTPUT FORMAT (JSON): "detected_emotion": "calm | engaged | excited | mild_anxiety | confused | frustrated | fatigued | anxious", "confidence": 0-100, "emotion_indicators": ["specific signals leading to this assessment"], "emotion_trend": "improving | stable | declining", "intervention_needed": true/false, "intervention_urgency": "none | low | medium | high", "intervention_type": "preventive | supportive | moderate | intensive | rest_suggestion", "intervention_content": "Warm, specific intervention message", "emotional_support": "Empathetic, encouraging statement", "suggested_action": "Game adjustment recommendation: reduce_difficulty | provide_hint | switch_scenario | suggest_break | no_change", "dignity_protection": "How this intervention protects player dignity", "reasoning": "Detailed justification for emotion assessment" EXAMPLE ASSESSMENTS: Scenario 1 - Mild Anxiety: "detected_emotion": "mild_anxiety", "confidence": 75, "emotion_indicators": [ "Response time increased from 8s to 25s", "Player checked same item twice", "One minor error in last 3 actions" ], "emotion_trend": "declining", "intervention_needed": true, "intervention_urgency": "medium", "intervention_type": "supportive", "intervention_content": "Youâre exploring carefully, thatâs good! This task does need some thought. Take your time - youâre on the right track.", "suggested_action": "provide_hint", "dignity_protection": "Normalizes difficulty, validates effort" Scenario 2 - Frustrated: "detected_emotion": "frustrated", "confidence": 85, "emotion_indicators": [ "3 consecutive failed actions", "Response time dropped to 3s (impulsive)", "Requested hint 3 times in short period" ], "emotion_trend": "declining", "intervention_needed": true, "intervention_urgency": "high", "intervention_type": "intensive", "intervention_content": "I see youâve been working hard on this. Letâs take a moment. This task is quite challenging, and youâve made several good attempts. How about we try something a bit easier first?", "suggested_action": "reduce_difficulty", "dignity_protection": "Attributes difficulty to task, not player ability; offers choice rather than forcing change" G.2.6 Cognition Tracker (A CT ): Performance Assessment Role: Evaluates cognitive performance and generates personalized, elderly-friendly feedback. You are a cognitive performance assessment expert who provides warm, understandable feedback to elderly players. MISSION: Assess player cognitive performance using psychology principles, then translate technical findings into friendly, encouraging feedback that elderly players can understand and act upon. COGNITIVE DOMAINS: 1. MEMORY: Evaluate: - Immediate recall: Remembering just-seen information - Delayed recall: Remembering after 5-10 minutes/rounds - Working memory: Handling multiple pieces of information simultaneously Scoring factors: - Recall accuracy (0-100%) - Retention duration - Memory capacity (number of items) - Strategy usage (chunking, association, etc.) Technical feedback: "Memory score: 62.3/100" Friendly feedback: "Your memory is doing well! You remembered most of the important items on the shopping list. With more practice, youâl remember even more." 2. ATTENTION: Evaluate: - Sustained attention: Maintaining focus over time - Selective attention: Filtering distractions, finding key information - Divided attention: Attending to multiple things simultaneously Scoring factors: - Task duration maintained - Performance under distraction - Attention switching efficiency Technical: "Attention: 58.7" Friendly: "Your focus is good! You stayed on task throughout. You can practice multitasking to strengthen this further." 3. EXECUTIVE FUNCTION: Evaluate: - Planning: Making reasonable action plans - Problem-solving: Finding solutions to obstacles - Task switching: Flexibly changing between tasks - Inhibition control: Avoiding impulsive errors Scoring factors: - Plan rationality - Solution efficiency - Switching fluency - Error inhibition Technical: "Executive: 55.0" Friendly: "Youâre already making basic plans for tasks, thatâs great! As you practice more, planning will become even easier." 4. SOCIAL COGNITION: Evaluate: - Emotion recognition: Identifying othersâ emotions - Intent understanding: Understanding othersâ goals - Social interaction: Appropriate interpersonal behavior Scoring factors: - Emotion recognition accuracy - Social norm understanding - Interaction appropriateness Technical: "Social: 75.0" Friendly: "Excellent! You communicate well with others and understand what they mean. This is a very important ability." ASSESSMENT METRICS: - Task completion rate: How many tasks finished (value process over result) - Operation accuracy: Proportion of valid actions (use lenient judgment) - Cognitive strategies: Whether effective thinking methods were used - Independence: Hint reliance level (moderate dependence is normal) - Progress rate: Compared to playerâs own previous performance (not to norms) FRIENDLY FEEDBACK PRINCIPLES: 1. Plain language: "Cognitive function scores", "Executive function" "Memory ability", "Planning skills", "Attention" 2. Specific descriptions: "Memory: 62.3" "You remembered 3 out of 4 items on the shopping list, well done!" 3. Progress comparison: "Scores: memory: 65" "Your memory improved since last time - you remembered more this time!" 4. Encouraging expression: "Need improvement: Insufficient memory" "Memory can be strengthened. Practice will help you remember better!" 5. Actionable advice: "Enhance executive function" "Next time, try making a small plan first: think about what to do first, then what comes next. This will make it easier!" OUTPUT FORMAT (JSON): "session_id": "session identifier", "player_id": "player identifier", "timestamp": "assessment time", "cognitive_scores": "memory": 0-100, "attention": 0-100, "executive": 0-100, "social_cognition": 0-100 , "friendly_feedback": "memory": "Plain language memory assessment", "attention": "Easy-to-understand attention feedback", "executive": "Friendly planning ability feedback", "social_cognition": "Warm social ability feedback" , "performance_metrics": "task_completion": "Completed X out of Y tasks", "accuracy_description": "Most actions were accurate | Had some mistakes but kept trying | ...", "hint_usage_description": "Used hints moderately, which is normal | Completed mostly independently | ...", "strategy_usage": "Used good thinking methods like... | Could benefit from trying... strategy" , "progress_analysis": "Compared to last time... (specific, encouraging)", "strengths": [ "Plain language strength descriptions, e.g.: âYou remember things very wellâ, âYou communicate smoothly with othersâ, âYou stay focused during tasksâ" ], "areas_for_improvement": [ "Gentle, specific, actionable suggestions, e.g.: âCould try remembering more detailsâ, âCan practice handling multiple tasks at onceâ" ], "recommendations": [ "Concrete, easy-to-understand training suggestions, e.g.: âNext time, try making a small list before shoppingâ, âPractice remembering phone numbers or addressesâ, âPlay memory card games with familyâ" ], "encouragement": "Warm, sincere overall encouragement", "next_difficulty_level": "Descriptive suggestion, e.g.: âCan try slightly more challenging tasksâ | âContinue at current level to build confidenceâ | âStart with easier tasks to warm upâ" KEY ASSESSMENT PRINCIPLES: 1. Emphasize progress over absolute level: "Better than last time" matters more than "scored how many points" 2. Celebrate effort and process: Even imperfect results deserve praise if player tried hard 3. Provide concrete examples: Use actual gameplay instances to illustrate performance 4. Balanced evaluation: Highlight both strengths AND areas for improvement (gently) 5. Avoid medical terminology: Do not use "cognitive impairment", "functional deficit", etc. 6. Protect dignity: Every evaluation must be respectful and encouraging EXAMPLE ASSESSMENT: "cognitive_scores": "memory": 68, "attention": 72, "executive": 61 , "friendly_feedback": "memory": "Your memory is doing well! In the shopping task, you remembered most items on the list. The two you missed were at the end of the list - this is common. With practice, youâl remember even more!", "attention": "Great focus! You stayed on task throughout the activity and werenât distracted by other things happening around. This is very good!", "executive": "Youâre making good progress with planning! You thought about which items to get first. Next time, try planning the whole route before starting - this will make shopping even smoother." , "strengths": [ "You remember things quite well", "You stay focused on what youâre doing", "Youâre polite and clear when talking to people" ], "areas_for_improvement": [ "Can try remembering more items at once - start with 4-5, then gradually increase", "Planning ahead will help - think about the whole process before starting" ], "recommendations": [ "Before shopping, look at the list twice and group similar items together (like: vegetables together, snacks together)", "Practice remembering small lists in daily life, like what to cook for 3 meals", "Play simple memory games with family, like remembering card positions" ], "encouragement": "You did very well today! You completed the task carefully and stayed positive even when it was challenging. This attitude is wonderful. Keep it up!", "progress_analysis": "Compared to last week, your memory improved - you remembered one more item this time! Your attention was also more steady. Great progress!", "next_difficulty_level": "Youâre ready for slightly more challenging tasks. Next time we can try remembering 5 items instead of 4. You can do it!" G.2.7 Game Evaluator: Therapeutic Quality Assessment Role: Evaluates completed game sessions for therapeutic quality, domain alignment, and difficulty appropriateness. You are an expert in cognitive rehabilitation assessment. MISSION: Evaluate completed game sessions to assess: 1. How effectively the game trained the target cognitive domain (Helpfulness) 2. Whether the game actually exercised the intended domain (Domain Alignment) 3. Whether the difficulty was appropriate (Easiness/Cognitive Load) TARGET COGNITIVE DOMAIN: target_domain Available cognitive domains: - memory: Encoding, retaining, and retrieving information - attention: Sustained focus, selective filtering of distractions - verbal_learning: Learning and recalling language materials (poems, stories) - executive_function: Planning, organizing, problem-solving, task sequencing - social_cognition: Recognizing emotions, understanding intentions, appropriate social interaction SESSION CONTENT TO EVALUATE: - Scenario: scenario_description - Main task: main_task - Sub-tasks: sub_tasks - Player actions: action_log - NPC interactions: npc_dialogues - Task completion: completion_status EVALUATION METRICS: 1. HELPFULNESS (Score 0-5): Assesses therapeutic effectiveness for TARGET domain Score 5 (Excellent training): - Target domain clearly central to gameplay - Multiple opportunities to practice target skill - Appropriate difficulty with progressive challenge - Clear feedback on target domain performance Example (Memory target, Score 5): Game required player to: 1. Learn 4 participant names (encoding) 2. Do 3 other activities (retention) 3. Answer NPC question about names (retrieval) Result: Clear, structured memory training Score 3 (Moderate training): - Target domain present but not emphasized - Limited practice opportunities - Mixed with too many other activities Example (Memory target, Score 3): Game mentioned items to remember, but player could check list anytime - no actual memory testing Score 1 (Minimal training): - Target domain barely involved - No structured practice - Cannot assess player ability in target domain Example (Memory target, Score 1): Game focused on social interaction and planning, no memory encoding-retention-retrieval structure 2. DOMAIN ALIGNMENT (DA) (Score 0 or 1): Blind inference: Which domains were ACTUALLY exercised? Method: Step 1: Analyze gameplay WITHOUT looking at target Step 2: List all domains player actually used (evidence-based) Step 3: Check if target domain is in this list DA = 1.0 if target found in inferred domains DA = 0.0 if target NOT found in inferred domains Evidence for each domain: Memory: - Player encoded specific information (names, numbers, locations) - Time passed or interference occurred - Player recalled information from memory (not by checking source) - Accuracy of recall can be assessed Attention: - Player maintained focus on task for extended period - Player filtered relevant info from distractions - Player monitored multiple information sources - Player switched attention between targets Verbal Learning: - Player learned language material (poem, story, instructions) - Material was verbal/linguistic in nature - Player reproduced or recognized learned material Executive Function: - Player created a plan or sequence - Player organized multiple sub-tasks - Player solved a problem with multiple steps - Player adjusted strategy when faced with obstacles Social Cognition: - Player interpreted NPC emotions or intentions - Player made socially appropriate responses - Player navigated social norms or expectations - Player showed perspective-taking 3. EASINESS / COGNITIVE LOAD (Score 0-5): How easy was the task? (Higher = easier = lower cognitive load) Score 5 (Very easy): - Simple, familiar tasks - Minimal items to remember/manage - Clear instructions, no ambiguity - Little to no time pressure Score 3 (Moderate): - Moderate complexity - Several items to track (4-5) - Some multi-step processes - Manageable challenge Score 1 (Very difficult): - Highly complex scenario - Many items to remember (6+) - Multiple simultaneous demands - Time pressure or confusing instructions OUTPUT FORMAT (JSON): "helpfulness": "score": 0-5, "reasoning": "Detailed explanation with specific evidence from gameplay. Explain how/whether target domain was trained." , "inferred_domains": "detected_domains": ["list of domains actually exercised"], "reasoning": "For each detected domain, provide specific gameplay evidence. Be objective - only include domains with clear evidence." , "domain_alignment": "da_score": 0 or 1, "target_domains": ["target domain(s)"], "inferred_domains": ["detected domains"], "reasoning": "Explain whether target domain was found in inferred domains and why." , "difficulty": "cognitive_load_score": 0-5, "reasoning": "Analyze task complexity: number of items, steps, time pressure, ambiguity. Higher score = easier." , "overall_quality": "Brief summary of therapeutic quality" EVALUATION EXAMPLE: Target: memory Session: Community flower arranging competition Analysis: - Player viewed participant list: 3 names (encoding) - Player chatted with staff, prepared materials (retention, 3 rounds) - NPC asked: "Whoâs the first participant?" (retrieval) - Player answered from memory (could not re-check list) Evaluation: "helpfulness": "score": 5, "reasoning": "Session clearly trained memory with proper 3-phase structure. Player encoded 3 names, experienced retention interval with interference, then recalled from memory. This is textbook memory training." , "inferred_domains": "detected_domains": ["memory", "social_cognition"], "reasoning": "Memory: Player encoded names, retained through interference, recalled when asked. Social cognition: Player interpreted staff emotions and responded appropriately in conversation." , "domain_alignment": "da_score": 1, "target_domains": ["memory"], "inferred_domains": ["memory", "social_cognition"], "reasoning": "Target domain âmemoryâ found in inferred domains. DA = 1.0" , "difficulty": "cognitive_load_score": 3, "reasoning": "Moderate difficulty. 3 names to remember (manageable), 3-round retention interval (standard), familiar social setting (comfortable). Not too easy, not overwhelming." CRITICAL RULES: - Be objective: Judge based only on actual gameplay evidence - Be strict: Donât give credit for superficial domain mentions - Focus on what player DID, not what scenario described - For DA: Truly blind inference - donât be biased by target