Paper deep dive
Plagiarism or Productivity? Students Moral Disengagement and Behavioral Intentions to Use ChatGPT in Academic Writing
John Paul P. Miranda, Rhiziel P. Manalese, Mark Anthony A. Castro, Renen Paul M. Viado, Vernon Grace M. Maniago, Rudante M. Galapon, Jovita G. Rivera, Amado B. Martinez
Intelligence
Status: succeeded | Model: google/gemini-3.1-flash-lite-preview | Prompt: intel-v1 | Confidence: 93%
Last extracted: 3/23/2026, 12:06:24 PM
Summary
This study investigates the influence of moral disengagement mechanisms (moral justification, euphemistic labeling, displacement of responsibility, minimizing consequences, and attribution of blame) on Filipino college students' intentions to use ChatGPT for academic writing, integrating these factors into the Theory of Planned Behavior (TPB). Results from 418 participants indicate that moral disengagement significantly predicts attitudes, subjective norms, and perceived behavioral control, with attribution of blame being a primary driver. The findings highlight the need for clearer institutional academic integrity policies and ethical guidance regarding AI use in higher education.
Entities (4)
Relation Signals (3)
Attitudes â predicts â Behavioral Intention
confidence 95% ¡ attitudes had the highest impact on behavioral intention
Attribution of Blame â influences â Behavioral Intention
confidence 90% ¡ Among the predictors, attribution of blame had the strongest influence
Moral Disengagement â predicts â Attitudes
confidence 90% ¡ moral disengagement mechanisms influenced students' attitudes and sense of control
Cypher Suggestions (0)
No Cypher suggestions yet.
Abstract
Abstract:This study examined how moral disengagement influences Filipino college students' intention to use ChatGPT in academic writing. The model tested five mechanisms: moral justification, euphemistic labeling, displacement of responsibility, minimizing consequences, and attribution of blame. These mechanisms were analyzed as predictors of attitudes, subjective norms, and perceived behavioral control, which then predicted behavioral intention. A total of 418 students with ChatGPT experience participated. The results showed that several moral disengagement mechanisms influenced students' attitudes and sense of control. Among the predictors, attribution of blame had the strongest influence, while attitudes had the highest impact on behavioral intention. The model explained more than half of the variation in intention. These results suggest that students often rely on institutional gaps and peer behavior to justify AI use. Many believe it is acceptable to use ChatGPT for learning or when rules are unclear. This shows a need for clear academic integrity policies, ethical guidance, and classroom support. The study also recognizes that intention-based models may not fully explain student behavior. Emotional factors, peer influence, and convenience can also affect decisions. The results provide useful insights for schools that aim to support responsible and informed AI use in higher education.
Tags
Links
- Source: https://arxiv.org/abs/2603.19549v1
- Canonical: https://arxiv.org/abs/2603.19549v1
Trouble viewing inline? Open PDF directly â
Full Text
32,991 characters extracted from source content.
Expand or collapse full text
2025 International Workshop on Artificial Intelligence and Education (WAIE) https://doi.org/10.1109/WAIE67422.2025.11381217 This is a pre-copyedited version of a paper published in the Proceedings of the 2025 International Workshop on Artificial Intelligence and Education (WAIE). The final authenticated version is available online at https://doi.org/10.1109/WAIE67422.2025.11381217. Any reproduction or distribution of this paper in any form is not permitted without written permission from the author and the publisher. Conference Paper Plagiarism or Productivity? Students Moral Disengagement and Behavioral Intentions to Use ChatGPT in Academic Writing John Paul P. Miranda 1* , Rhiziel P. Manalese 1 , Mark Anthony A. Castro 1 , Renen Paul M. Viado 2 , Vernon Grace M. Maniago 1 , Rudante M. Galapon 2 Jovita G. Rivera 1 , Amado B. Martinez, Jr. 1 1. Pampanga State University, Pampanga, Philippines 2. National University, DasmariĂąas, Philippines * Correspondence: John Paul P. Miranda, Pampanga State University, jppmiranda@pampangastateu.edu.ph How to cite this article: Miranda, J. P. P., Manalese, R. P., Castro, M. A. A, Viado, R. P. M., Maniago, V. G. M., Galapon, R. M., Rivera, J. G., & Martinez Jr., A. B. (2025). Plagiarism or Productivity? Students Moral Disengagement and Behavioral Intentions to Use ChatGPT in Academic Writing. 2025 International Workshop on Artificial Intelligence and Education (WAIE) (p. 383-387). IEEE. https://doi.org/10.1109/WAIE67422.2025.11381217 Article History: Submitted: 06 June 2025 Accepted: 09 July 2025 Published: 17 February 2026 ABSTRACT This study examined how moral disengagement influences Filipino college studentsâ intention to use ChatGPT in academic writing. The model tested five mechanisms: moral justification, euphemistic labeling, displacement of responsibility, minimizing consequences, and attribution of blame. These mechanisms were analyzed as predictors of attitudes, subjective norms, and perceived behavioral control, which then predicted behavioral intention. A total of 418 students with ChatGPT experience participated. The results showed that several moral disengagement mechanisms influenced studentsâ attitudes and sense of control. Among the predictors, attribution of blame had the strongest influence, while attitudes had the highest impact on behavioral intention. The model explained more than half of the variation in intention. These results suggest that students often rely on institutional gaps and peer behavior to justify AI use. Many believe it is acceptable to use ChatGPT for learning or when rules are unclear. This shows a need for clear academic integrity policies, ethical guidance, and classroom support. The study also recognizes that intention-based models may not fully explain student behavior. Emotional factors, peer influence, and convenience can also affect decisions. The results provide useful insights for schools that aim to support responsible and informed AI use in higher education. Keywords: ChatGPT, moral disengagement, academic integrity, behavioral intention, Theory of Planned Behavior Miranda et al. (2025) 2025 International Workshop on Artificial Intelligence and Education (WAIE) https://doi.org/10.1109/WAIE67422.2025.11381217 Page 2 of 9 INTRODUCTION Several studies have reported that students increasingly use ChatGPT to draft or revise academic assignments (Baek et al., 2024; Stojanov et al., 2024). Filipino students also adopt generative artificial intelligence (Gen AI) to generate ideas, organize essays, and improve written outputs (Espartinez, 2025; Giray & and Aquino, 2024). For example, (Liu et al., n.d.) found that instruction supported by large language models improved writing performance, self-regulated learning strategies, and motivation among EFL students. Despite these benefits, scholars have expressed concerns about how widespread AI use challenges traditional definitions of authorship, originality, and academic integrity (Yeo, 2023), (Chan, 2025). In response, Philippine universities continue to revise and update honor codes and plagiarism policies to address the evolving ethical environment (De La Salle University, 2025; Orbe & Santos, 2022; University of the Philippines, 2023). Moral disengagement theory provides a framework for understanding how students justify behaviors that may violate academic integrity (Raney, 2020). The theory proposes that individuals rely on cognitive mechanisms such as moral justification, euphemistic labeling, displacement of responsibility, minimizing consequences, and attribution of blame to reduce internal moral conflict when engaging in questionable actions (Raney, 2020; Waqas et al., 2025). Furthermore, (Qu & Wang, 2025) observed that students may interpret AI-generated text as a neutral learning tool, which allows them to avoid feelings of guilt. These cognitive strategies may lead students to consider AI-assisted writing acceptable even when institutional policies emphasize academic honesty. Although moral disengagement offers valuable insights, understanding studentsâ intentions to use ChatGPT also requires attention to other psychological factors. The Theory of Planned Behavior (TPB) explains that behavioral intention results from attitudes toward the behavior, perceived social norms, and perceived behavioral control. Recent studies have confirmed the relevance of TPB in predicting technology adoption within educational contexts (Chu & Chen, 2016; Naseri & Abdullah, 2024). However, these models have not fully considered how moral reasoning may influence the TPB components. In the Philippine context, (Martinez, 2025) reported rapid AI adoption among students (Villarino, 2025), while (Bozkurt et al., 2024) identified ongoing uncertainty regarding ethical policies for AI use. Despite the growing integration of AI tools in higher education, limited research has explored how moral disengagement mechanisms interact with TPB constructs to shape studentsâ intentions to adopt generative AI tools. The present study addresses this gap by integrating moral disengagement theory and the Theory of Planned Behavior to examine how Filipino college students form intentions to use ChatGPT for academic writing. The study focuses on students who have experience using ChatGPT in assignments, theses, or feasibility studies. It applies a cross-sectional design and structural equation modeling to estimate the relationships among moral disengagement mechanisms, TPB components, and behavioral intention. The findings aim to clarify the psychosocial processes behind AI adoption and to provide empirical evidence that can inform institutional policies, instructional practices, and ethical guidelines in Philippine higher education. THEORETICAL FRAMEWORK AND HYPOTHESES The present study integrates moral disengagement theory and the Theory of Planned Behavior to examine studentsâ intentions to use ChatGPT for academic writing. Moral disengagement theory explains how individuals rationalize potentially unethical behaviors through cognitive mechanisms (Moore, 2015; Raney, 2020; Zhang et al., 2025) such as moral justification (reframing ChatGPT use as educationally beneficial), euphemistic labeling (softening the ethical implications), displacement of responsibility (shifting blame to external factors like instructors or unclear policies), minimizing consequences (downplaying harm), and attribution of blame Miranda et al. (2025) 2025 International Workshop on Artificial Intelligence and Education (WAIE) https://doi.org/10.1109/WAIE67422.2025.11381217 Page 3 of 9 (assigning fault to institutional or systemic shortcomings). These mechanisms allow students to cognitively justify their ChatGPT use despite academic integrity guidelines. The TPB posits that attitudes (studentsâ positive or negative evaluations), subjective norms (perceived social pressures), and perceived behavioral control (confidence in managing ChatGPT use ethically) directly influence behavioral intention to use ChatGPT (Ahadzadeh et al., 2024; Al-Qaysi et al., 2025; Tran et al., 2024). In this integrated framework, moral disengagement mechanisms are conceptualized as distal predictors that influence the three TPB components, which subsequently predict behavioral intention. Accordingly, six hypotheses were proposed: (H1) moral disengagement mechanisms predict attitudes; (H2) moral disengagement mechanisms predict subjective norms; (H3) moral disengagement mechanisms predict perceived behavioral control; (H4) attitudes predict behavioral intention; (H5) subjective norms predict behavioral intention; and (H6) perceived behavioral control predicts behavioral intention. METHODOLOGY Participants and Procedure The sample included 418 college students from different academic programs in the Philippines. All participants had prior experience using ChatGPT for academic writing. Their academic tasks included assignments, feasibility studies, case studies, and theses. Participation was voluntary, and informed consent was obtained. The survey was conducted online using institutional platforms and social media. The study followed a cross-sectional design and collected studentsâ self-reported data on their perceptions, attitudes, and behaviors related to ChatGPT use. The students had a mean age of 21.65 years. The sample included both male and female respondents. Most of them studied in public higher education institutions. Students reported different frequencies and purposes for using ChatGPT in their academic tasks. Most students reported regular internet access for academic work. The self-rated academic integrity awareness score was 3.92, which indicates a moderately high level of perceived awareness of academic integrity standards. Table 1. Demographic profile (N = 418) Variable n % Sex - Male 245 58.5 - Female 174 41.5 Enrolled in - Public 359 85.7 - Private 60 14.3 Type of Scholarly Work* - Thesis 143 34.1 - Feasibility study 95 22.7 - Case analysis 100 23.9 - Capstone project 147 35.1 - Research report or term paper 166 39.6 - Market study 64 15.3 - Action research 59 14.1 - Others 35 8.37 Frequency of using ChatGPT for academic work - Once 9 2.1 Miranda et al. (2025) 2025 International Workshop on Artificial Intelligence and Education (WAIE) https://doi.org/10.1109/WAIE67422.2025.11381217 Page 4 of 9 Variable n % - Rarely 87 20.8 - Monthly 101 24.1 - Weekly 175 41.8 - Daily 47 11.2 Purpose of using ChatGPT* - Ideas 342 81.6 - Drafts 154 36.8 - Rewriting/ editing 192 45.8 - Referencing 123 29.4 - Summarizing 191 45.6 Regular access to internet for academic work - Yes 361 86.2 - No 58 13.8 Note: *Multiple answers Instrument The survey measured nine constructs based on moral disengagement theory and the theory of planned behavior, all contextualized to studentsâ use of ChatGPT for academic writing. All items used a five-point Likert scale where 1 indicated strong disagreement and 5 indicated strong agreement. Moral justification (3 items) measured studentsâ beliefs that using ChatGPT is acceptable if they perceive educational benefits or learning intentions. Euphemistic labeling (3 items) measured how students framed ChatGPT use as academic support rather than misconduct. Displacement of responsibility (3 items) measured how students shifted responsibility to others, such as instructors or peers. Minimizing consequences (3 items) measured beliefs that using ChatGPT causes minimal harm or does not violate academic integrity as long as students understand or modify the content. Attribution of blame (3 items) measured how students assigned responsibility to institutional policies, unclear guidelines, or external pressures. Attitudes toward ChatGPT use (3 items) measured studentsâ positive or negative evaluations of using ChatGPT in academic writing. Subjective norms (3 items) measured studentsâ perceptions of peer influence and social approval regarding ChatGPT use in academic tasks. Perceived behavioral control (3 items) measured studentsâ confidence in their ability to use ChatGPT ethically and manage its use appropriately. Behavioral intention (3 items) measured studentsâ intentions to continue using ChatGPT in future academic writing activities. The instrument also collected demographic information, including degree program, year level, and the type of academic work where ChatGPT was used. The instrument underwent pilot testing to ensure clarity and appropriateness of the items. Reliability testing showed that all constructs achieved acceptable internal consistency, with Cronbachâs alpha values greater than .70. Data Analysis Composite scores were calculated by averaging the items under each construct. All variables were standardized before analysis to obtain standardized coefficients. The study used multiple linear regression analyses to examine the hypothesized relationships. Four separate regression models were conducted. The first model tested whether moral disengagement mechanisms predicted attitudes. The second model tested whether moral disengagement mechanisms predicted subjective norms. The third model tested whether moral disengagement mechanisms predicted perceived behavioral control. The fourth model tested whether attitudes, subjective norms, and perceived behavioral control predicted behavioral intention. All regression models used ordinary least squares estimation. The analysis was conducted using Python in Jupyter Notebook. Miranda et al. (2025) 2025 International Workshop on Artificial Intelligence and Education (WAIE) https://doi.org/10.1109/WAIE67422.2025.11381217 Page 5 of 9 RESULTS AND DISCUSSION Descriptive Statistics Table 2 presents the overall mean and standard deviation for each construct. The students generally agreed that using ChatGPT is acceptable if they still include their original thinking (M = 3.78, SD = 1.06), that it is not unethical if ChatGPT helps explain ideas better (M = 3.49, SD = 1.07), and that using ChatGPT is fine if the goal is to learn, not to cheat (M = 3.93, SD = 1.10). They considered ChatGPT a tool for support rather than cheating (M = 3.91, SD = 1.11), viewed asking ChatGPT for ideas like discussing with a classmate (M = 3.73, SD = 1.10), and agreed that rewording ChatGPTâs output makes it acceptable to use (M = 3.41, SD = 1.09). The respondents reported that many students in their class use ChatGPT, so they also use it (M = 3.36, SD = 1.15), rely on ChatGPT more when instructors do not provide clear rules (M = 3.22, SD = 1.13), and do not feel fully responsible for using ChatGPT since it is widely used (M = 3.12, SD = 1.04). They stated that using ChatGPT is not a serious issue if they understand the content (M = 3.63, SD = 1.04), that AI-assisted writing is less harmful than copying from another student (M = 3.39, SD = 1.09), and that it is a small issue to use ChatGPT if the work remains mostly theirs (M = 3.54, SD = 1.04). The participants reported that students use ChatGPT more when schools give unclear policies (M = 3.44, SD = 1.07), that they use ChatGPT more when the academic workload becomes overwhelming (M = 3.59, SD = 1.05), and that the teachers should guide students better if AI use is discouraged (M = 3.70, SD = 1.09). They agreed that ChatGPT helps improve the quality of academic work (M = 3.61, SD = 1.03), that it makes writing tasks easier (M = 3.54, SD = 1.05), and that they feel positive about using ChatGPT in school assignments (M = 3.37, SD = 1.10). Many participants agreed that students they know use ChatGPT for academic writing (M = 3.84, SD = 1.05), that using ChatGPT for projects is common in their school (M = 3.69, SD = 1.09), and that they feel social pressure to use ChatGPT because others do (M = 3.09, SD = 1.15). They reported that they know how to use ChatGPT without violating school rules (M = 3.70, SD = 1.06), feel confident in using ChatGPT ethically (M = 3.52, SD = 1.04), and can recognize when ChatGPT use becomes academically dishonest (M = 3.62, SD = 1.02). The respondents expressed intentions to use ChatGPT in future academic activities (M = 3.35, SD = 1.07), to continue using ChatGPT even for major requirements (M = 3.25, SD = 1.13), and to use ChatGPT unless strict rules prohibit it (M = 3.51, SD = 1.11). Table 2. Descriptive statistics of constructs Construct Mean SD Moral Justification 3.73 0.91 Euphemistic Labeling 3.68 0.95 Displacement of Responsibility 3.24 0.92 Minimizing Consequences 3.52 0.94 Attribution of Blame 3.58 0.91 Attitudes Toward ChatGPT Use 3.51 0.99 Subjective Norms 3.54 0.9 Perceived Behavioral Control 3.62 0.94 Behavioral Intentions 3.37 1.03 Predictors of Attitudes Toward ChatGPT Use A multiple regression analysis examined the extent to which moral justification, euphemistic labeling, displacement of responsibility, minimizing consequences, and attribution of blame predicted studentsâ attitudes toward ChatGPT use. The model was statistically significant, F(5, 412) = 173.90, p < .001, accounting for approximately 67.9% of the variance (R² = .679). Euphemistic labeling (β = 0.133, p = .010), Miranda et al. (2025) 2025 International Workshop on Artificial Intelligence and Education (WAIE) https://doi.org/10.1109/WAIE67422.2025.11381217 Page 6 of 9 displacement of responsibility (β = 0.117, p = .002), minimizing consequences (β = 0.313, p < .001), and attribution of blame (β = 0.319, p < .001) were significant predictors, while moral justification was not significant (β = 0.068, p = .178). These results suggest that students who soften the ethical implications of using ChatGPT, transfer responsibility to others, downplay possible academic harm, or assign fault to external academic structures tend to hold more favorable attitudes toward using ChatGPT for academic writing (Acosta-Enriquez, ArbulĂş Ballesteros, Arbulu Perez Vargas, et al., 2024; Acosta-Enriquez, ArbulĂş Ballesteros, HuamanĂ Jordan, et al., 2024). Minimizing consequences and attribution of blame produced the strongest effects. Students likely felt more comfortable using ChatGPT when they believed that institutional guidelines were unclear or the consequences of use were minor (Bikanga Ada, 2024; Hasanein & Sobaih, 2023). Moral justification did not predict attitudes, suggesting that students may not rely heavily on internal justifications but instead use external rationalizations. Predictors of Subjective Norms The regression model predicting subjective norms was significant, F(5, 412) = 107.80, p < .001, explaining 56.7% of the variance (R² = .567). Euphemistic labeling (β = 0.125, p = .038), displacement of responsibility (β = 0.149, p = .001), minimizing consequences (β = 0.169, p = .002), and attribution of blame (β = 0.387, p < .001) were significant predictors. Moral justification was not significant (β = 0.038, p = .521). The findings indicate that studentsâ perceptions of social acceptance for ChatGPT use relate closely to their moral disengagement rationalizations (Zhang et al., 2025). Attribution of blame had the strongest effect. Students who believed that institutions or instructors failed to provide clear guidance perceived stronger peer acceptance for ChatGPT use (Santos et al., 2024; Strzelecki, 2024). Euphemistic labeling, displacement of responsibility, and minimizing consequences also influenced subjective norms, suggesting that external rationalizations contributed to studentsâ sense that ChatGPT use was common and socially supported. Again, moral justification did not predict subjective norms, which shows that peer norms depend more on external explanations rather than internal ethical framing. Predictors of Perceived Behavioral Control The predictors also explained significant variance in perceived behavioral control, F(5, 412) = 119.00, p < .001, with R² = .591. Moral justification (β = 0.258, p < .001), minimizing consequences (β = 0.124, p = .017), and attribution of blame (β = 0.421, p < .001) emerged as significant predictors. Euphemistic labeling and displacement of responsibility were not significant. These results show that students who assign responsibility to institutions, minimize potential harm, or frame ChatGPT use as beneficial feel more confident in their ability to manage AI- assisted writing ethically (Blahopoulou & Ortiz-Bonnin, 2025; Cheng et al., 2025; Grimes & Barton, 2024). Attribution of blame served as the strongest predictor. This also means that students who blamed academic structures for lack of clarity or oversight felt more capable of using ChatGPT without crossing ethical boundaries. Minimizing consequences and moral justification also raised studentsâ perceived control by reducing the perceived risks or ethical concerns associated with ChatGPT use (Howlader et al., 2025; Zhang et al., 2025). Predictors of Behavioral Intention The attitudes, subjective norms, and perceived behavioral control significantly predicted behavioral intention to use ChatGPT, F(3, 414) = 212.10, p < .001, explaining 60.6% of the variance (R² = .606). Attitudes (β = 0.499, p < .001), subjective norms (β = 0.098, p = .037), and perceived behavioral control (β = 0.253, p < .001) all significantly predicted behavioral intention (Fig. 1). The results confirm that studentsâ behavioral intentions to use ChatGPT depend most strongly on their favorable evaluations of ChatGPT use. Attitudes served as the strongest predictor, which Miranda et al. (2025) 2025 International Workshop on Artificial Intelligence and Education (WAIE) https://doi.org/10.1109/WAIE67422.2025.11381217 Page 7 of 9 supports the Theory of Planned Behaviorâs assumption that attitudes are the most immediate determinant of behavioral intention. Subjective norms and perceived behavioral control also influenced intention. Students who perceived peer support and who felt confident in managing ChatGPT use expressed stronger intentions to continue using the tool for academic writing. Figure 1. Final structural model with standardized estimates CONCLUSION This study examined how moral disengagement influences the intention of Filipino college students to use ChatGPT in academic writing. The results showed that students who blamed institutions, minimized possible consequences, or shifted responsibility were more likely to form positive attitudes and feel confident using ChatGPT. Attitudes had the strongest influence on behavioral intention, followed by perceived control and social norms. These results suggest that studentsâ decisions to use AI tools are shaped by both their moral reasoning and the academic environment around them. These results raise ethical concerns that schools must address. Many students think it is acceptable to use ChatGPT if the purpose is learning or if they revise the content. Some students justify their actions by pointing to unclear school policies. This situation shows that many students do not have a clear understanding of academic honesty. Schools should update and clearly define academic honesty, especially in the context of AI use. They should not only provide rules but also give proper guidance to help students reflect on the moral aspects of using AI tools. Teachers can support this effort by leading classroom discussions, using real examples, and giving feedback. A strong academic culture that promotes honesty, accountability, and responsibility can help students make better decisions when using AI in their academic work. This study also shows that existing models like the TPB do not fully explain student behavior in real situations. The model assumes that students always act based on logic, but other factors like stress, pressure, or ease of access can lead to choices that are not fully planned. Emotional and cultural factors also play a role in how students view fairness and responsibility. Future studies should explore these areas to build better models that match how students behave in digital learning environments. This study supports the need for clear rules, faculty guidance, and programs that develop moral responsibility in students who use AI tools. REFERENCES Acosta-Enriquez, B. G., ArbulĂş Ballesteros, M. A., Arbulu Perez Vargas, C. G., Orellana Ulloa, M. N., GutiĂŠrrez Ulloa, C. R., Pizarro Romero, J. M., GutiĂŠrrez Jaramillo, N. D., Cuenca Orellana, H. U., Ayala Miranda et al. (2025) 2025 International Workshop on Artificial Intelligence and Education (WAIE) https://doi.org/10.1109/WAIE67422.2025.11381217 Page 8 of 9 AnzoĂĄtegui, D. X., & LĂłpez Roca, C. (2024). Knowledge, attitudes, and perceived Ethics regarding the use of ChatGPT among generation Z university students. International Journal for Educational Integrity, 20(1), 10. https://doi.org/10.1007/s40979-024-00157-4 Acosta-Enriquez, B. G., ArbulĂş Ballesteros, M. A., HuamanĂ Jordan, O., LĂłpez Roca, C., & Saavedra Tirado, K. (2024). Analysis of college studentsâ attitudes toward the use of ChatGPT in their academic activities: effect of intent to use, verification of information and responsible use. BMC Psychology, 12(1), 255. https://doi.org/10.1186/s40359-024-01764-z Ahadzadeh, A. S., Wu, S. L., & Sijia, X. (2024). Exploring Academic Intentions for ChatGPT : A Perspective from the Theory of Planned Behavior ABSTRACT. 11(2), 1â22. Al-Qaysi, N., Mostafa, A.-E., Mohammed A., A.-S., Mohammad, I., Azhana, A., & and Mahmoud, M. A. (2025). Determinants of ChatGPT Use and its Impact on Learning Performance: An Integrated Model of BRT and TPB. International Journal of HumanâComputer Interaction, 41(9), 5462â5474. https://doi.org/10.1080/10447318.2024.2361210 Baek, C., Tate, T., & Warschauer, M. (2024). âChatGPT seems too good to be trueâ: College studentsâ use and perceptions of generative AI. Computers and Education: Artificial Intelligence, 7, 100294. https://doi.org/https://doi.org/10.1016/j.caeai.2024.100294 Bikanga Ada, M. (2024). It Helps with Crap Lecturers and Their Low Effort: Investigating Computer Science Studentsâ Perceptions of Using ChatGPT for Learning. In Education Sciences (Vol. 14, Issue 10). https://doi.org/10.3390/educsci14101106 Blahopoulou, J., & Ortiz-Bonnin, S. (2025). Student perceptions of ChatGPT: benefits, costs, and attitudinal differences between users and non-users toward AI integration in higher education. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13575-9 Bozkurt, A., Xiao, J., Farrow, R., Bai, J. Y. H., Nerantzi, C., Moore, S., Dron, J., Stracke, C. M., Singh, L., Crompton, H., Koutropoulos, A., Terentev, E., Pazurek, A., Nichols, M., Sidorkin, A. M., Costello, E., Watson, S., Mulligan, D., Honeychurch, S., ... Asino, T. I. (2024). The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future. Open Praxis. https://doi.org/10.55982/openpraxis.16.4.777 Chan, C. K. Y. (2025). Studentsâ perceptions of âAI-giarismâ: investigating changes in understandings of academic misconduct. Education and Information Technologies, 30(6), 8087â8108. https://doi.org/10.1007/s10639-024-13151-7 Cheng, A., Calhoun, A., & Reedy, G. (2025). Artificial intelligence-assisted academic writing: recommendations for ethical use. Advances in Simulation, 10(1), 22. https://doi.org/10.1186/s41077-025-00350-6 Chu, T.-H., & Chen, Y.-Y. (2016). With Good We Become Good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92â93, 37â52. https://doi.org/https://doi.org/10.1016/j.compedu.2015.09.013 De La Salle University. (2025). DLSU releases policy on Artificial Intelligence use in education. 2401, 1â6. https://w.dlsu.edu.ph/wp-content/uploads/pdf/stratcom/2401/2025/2401V56N12.pdf Espartinez, A. S. (2025). Between Innovation and Tradition: A Narrative Inquiry of Studentsâ and Teachersâ Experiences with ChatGPT in Philippine Higher Education. In Social Sciences (Vol. 14, Issue 6). https://doi.org/10.3390/socsci14060359 Giray, L., & and Aquino, R. (2024). Use and impact of ChatGPT on undergraduate engineering students: A case from the Philippines. Internet Reference Services Quarterly, 28(4), 453â462. https://doi.org/10.1080/10875301.2024.2384028 Grimes, J. A. L., & Barton, A. K. (2024). Preparing for ChatGPT: Comparing Student Attitudes on Generative AI in Contrasting Class Instruction. 2024 South East Section Meeting, 40907. https://doi.org/10.18260/1-2--45552 Hasanein, A. M., & Sobaih, A. E. E. (2023). Drivers and Consequences of ChatGPT Use in Higher Education: Key Stakeholder Perspectives. In European Journal of Investigation in Health, Psychology and Education (Vol. 13, Issue 11, p. 2599â2614). https://doi.org/10.3390/ejihpe13110181 Howlader, M. H., Tohan, M. M., Zaman, S., Chanda, S. K., Jiaxin, G., & Rahman, M. A. (2025). Factors influencing the acceptance and usage of ChatGPT as an emerging learning tool among higher education students in Bangladesh: a structural equation modeling. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2504224 Liu, Z.-M., Gwo-Jen, H., Chuang-Qi, C., Xiang-Dong, C., & and Ye, X.-D. (n.d.). Integrating large language Miranda et al. (2025) 2025 International Workshop on Artificial Intelligence and Education (WAIE) https://doi.org/10.1109/WAIE67422.2025.11381217 Page 9 of 9 models into EFL writing instruction: effects on performance, self-regulated learning strategies, and motivation. Computer Assisted Language Learning, 1â25. https://doi.org/10.1080/09588221.2024.2389923 Martinez, A. L. S. (2025). More Filipino students now using AI for learning. Business World. https://w.bworldonline.com/technology/2025/06/12/678582/more-filipino-students-now-using- ai-for-learning/#google_vignette Moore, C. (2015). Moral disengagement. Current Opinion in Psychology, 6, 199â204. https://doi.org/10.1016/j.copsyc.2015.07.018 Naseri, R. N. N., & Abdullah, M. S. (2024). Understanding AI Technology Adoption in Educational Settings: A Review of Theoretical Frameworks and their Applications. Information Management and Business Review, 16(3(I) SE-Research Paper). https://doi.org/10.22610/imbr.v16i3(I).3963 Orbe, M. C., & Santos, J. M. (2022). Promoting institutional values through the development of student academic integrity statements. 10th MAAP Research Colloquium, 1â13. https://w.researchgate.net/publication/381582940_Promoting_institutional_values_through_th e_development_of_student_academic_integrity_statements Qu, Y., & Wang, J. (2025). The Impact of AI Guilt on Studentsâ Use of ChatGPT for Academic Tasks: Examining Disciplinary Differences. Journal of Academic Ethics. https://doi.org/10.1007/s10805- 025-09643-x Raney, A. A. (2020). Moral Disengagement. In The International Encyclopedia of Media Psychology (p. 1â 6). https://doi.org/https://doi.org/10.1002/9781119011071.iemp0207 Santos, M. J. D., Roldan, C. B. B., Pepito, G. T., & Lim-Cheng, N. R. T. (2024). Integrity in the Age of AI: Ethical Considerations of ChatGPT Use Among De La Salle University Senior High School Students. DLSU Research Congress 2024, 1â7. https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=2382&context=conf_shsrescon Stojanov, A., Liu, Q., & Koh, J. H. L. (2024). University studentsâ self-reported reliance on ChatGPT for learning: A latent profile analysis. Computers and Education: Artificial Intelligence, 6, 100243. https://doi.org/https://doi.org/10.1016/j.caeai.2024.100243 Strzelecki, A. (2024). Studentsâ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology. Innovative Higher Education, 49(2), 223â245. https://doi.org/10.1007/s10755-023-09686-1 Tran, D. H. T., Lee, Y.-F., Hung, H. S., Kao, W.-C., & Nguyen, H. B. N. (2024). The Influence of Studentsâ Beliefs of ChatGPT on Their Intentions of Using ChatGPT in Learning Foreign Languages. International Journal of Information and Education Technology, 14(8), 1044â1051. https://doi.org/10.18178/ijiet.2024.14.8.2132 University of the Philippines. (2023). 2023 - UP Principles for Responsible Artificial Intelligence. https://legal.uplb.edu.ph/up-policies/2023-up-principles-for-responsible-artificial-intelligence/ Villarino, R. T. (2025). Artificial Intelligence (AI) integration in Rural Philippine Higher Education: Perspectives, challenges, and ethical considerations. IJERI: International Journal of Educational Research and Innovation, 23 SE-. https://doi.org/10.46661/ijeri.10909 Waqas, M., Alishba, H., & and Chunyan, X. U. (2025). Understanding AIgiarism in higher education: the lens of general AI attitudes and moral disengagement. Studies in Higher Education, 1â17. https://doi.org/10.1080/03075079.2025.2497479 Yeo, M. A. (2023). Academic integrity in the age of Artificial Intelligence (AI) authoring apps. TESOL Journal, 14(3), e716. https://doi.org/https://doi.org/10.1002/tesj.716 Zhang, L., Clinton, A., & and Pentina, I. (2025). Interplay of rationality and morality in using ChatGPT for academic misconduct. Behaviour & Information Technology, 44(3), 491â507. https://doi.org/10.1080/0144929X.2024.2325023