Generative AI Misuse and Rule Compliance Among University Students in Bhutan: Implications for Assessment and Institutional AI Policy
Article Sidebar
Main Article Content
Abstract
Background and Aim: Generative artificial intelligence (AI) use is expanding in Bhutanese universities while institutional policies remain under development. Guided by an extended Theory of Planned Behavior, this study examined factors associated with unauthorized AI use and intention to comply with institutional AI policies.
Materials and Methods: An anonymous, cross-sectional survey was conducted in June 2026 among a convenience sample of 713 Bhutanese university students. Seven-point Likert-type responses were analyzed using factor analyses, reliability assessments, correlations with false discovery rate correction, multiple and moderated regression, and bootstrapped indirect association tests. Robustness checks included robust standard errors and demographic controls.
Results: The models explained 24.5% of variance in AI misuse and 19.6% in compliance intention. Approximately 29% of respondents endorsed at least one misuse item. Misuse was associated with misuse acceptability, AI familiarity, academic pressure, moral rationalization, rule clarity, subjective norms, and learning impact. Compliance intention was associated with subjective norms, rule clarity, behavioral control, productive AI use, institutional enforcement, and moral rationalization. Institutional enforcement showed a small independent association with compliance intention (β = .086). Compliance intention was inversely associated with misuse (B = −0.10, p = .004). All six indirect associations were small, with bootstrap confidence intervals excluding zero. Demographic adjustment preserved findings; academic level showed no significant moderation.
Conclusion: Findings support evaluating assignment-specific AI guidance, coordinated workloads, classroom discussion of misuse rationalizations, and communicated institutional policies. Cross-sectional data preclude causal inference, and convenience sampling limits generalizability. These associations provide a basis for evaluating approaches to responsible AI governance.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright and License
Copyright of all articles published in the Journal of Education and Learning Reviews (JELR) is retained by the author(s).
All articles are distributed under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). Users may read, download, copy, distribute, and share the content for non-commercial purposes with proper attribution.
Modification, adaptation, or commercial use of the content is not permitted.
License details: https://creativecommons.org/licenses/by-nc-nd/4.0/
References
Adjei, S., Nortey, I. N. N., Norteye, S. B., & Agyeman, I. K. (2026). Generative AI adoption and governance in African higher education: A mixed-methods study of Ghanaian universities. Journal of Education and Learning Reviews, 3(5), Article e3787. https://doi.org/10.60027/jelr.2026.e3787
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Atikuzzaman, M., & Ahmed, S. M. Z. (2025). Attitudes toward plagiarism: A comparative analysis of the influence of demographic variables on university students in Bangladesh. Journal of Academic Ethics, 23(3), 1523–1543. https://doi.org/10.1007/s10805-025-09612-4
Baig, M., & Paparo, J. (2026). A preliminary investigation of intentions to engage in academic misconduct using generative artificial intelligence tools. Teaching in Higher Education, 31(7), 1327–1345. https://doi.org/10.1080/13562517.2026.2663440
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Beck, L., & Ajzen, I. (1991). Predicting dishonest actions using the theory of planned behavior. Journal of Research in Personality, 25(3), 285–301. https://doi.org/10.1016/0092-6566(91)90021-H
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294. https://doi.org/10.2307/1911963
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Cook, R. D. (1977). Detection of influential observation in linear regression. Technometrics, 19(1), 15–18. https://doi.org/10.1080/00401706.1977.10489493
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
Dendup, S. (2024, July 8). RUB drafting new policy for using generative AI to regulate academic writing. Bhutan Broadcasting Service. https://www.bbs.bt/206036/
Dorji, K. (2026). The impact of generative artificial intelligence on academic assessment in Bhutanese higher education: Lecturers’ perspectives. Asian Journal of Contemporary Education, 10(1), 72–88. https://doi.org/10.55493/5052.v10i1.5907
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Ghimire, S. N., Bhattarai, U., & Baral, R. K. (2024). Implications of ChatGPT for higher education institutions: Exploring Nepali university students’ perspectives. Higher Education Research & Development, 43(8), 1769–1783. https://doi.org/10.1080/07294360.2024.2366323
Giray, L., Jacob, J., Encanto, V., & Mansilungan, C. J. (2026). Cheating writing with generative AI: Exploring student motivations using the theory of planned behavior. Journal of Academic Ethics, 24(1), Article 19. https://doi.org/10.1007/s10805-025-09695-z
Gyamtso, D. C., Sherab, K., & Maxwell, T. W. (2017). Teacher learning in changing professional contexts: Bhutanese teacher educators and the Educating for GNH initiative. Cogent Education, 4(1), Article 1384637. https://doi.org/10.1080/2331186X.2017.1384637
Hadra, M., Cambridge, K., & Mesbah, M. (2026). Evaluating the accuracy and reliability of AI content detectors in academic contexts. International Journal for Educational Integrity, 22, Article 4. https://doi.org/10.1007/s40979-026-00213-1
Harding, T. S., Mayhew, M. J., Finelli, C. J., & Carpenter, D. D. (2007). The theory of planned behavior as a model of academic dishonesty in engineering and humanities undergraduates. Ethics & Behavior, 17(3), 255–279. https://doi.org/10.1080/10508420701519239
Hasib, M., & Islam, M. S. (2025). How university students in Bangladesh engage with ChatGPT: A qualitative study. PLOS ONE, 20(9), Article e0333089. https://doi.org/10.1371/journal.pone.0333089
Hawdon, J., Costello, M., & Reichelmann, A. V. (2026). Cheating with ChatGPT and techniques of neutralization. Deviant Behavior, 47(4), 664–685. https://doi.org/10.1080/01639625.2025.2456067
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Huang, D., Hash, N., Cummings, J. J., & Prena, K. (2025). Academic cheating with generative AI: Exploring a moral extension of the theory of planned behavior. Computers and Education: Artificial Intelligence, 8, Article 100424. https://doi.org/10.1016/j.caeai.2025.100424
Ismail, Z. S., & Maharani, R. A. (2026). Artificial intelligence in university learning: Student usage patterns, perceived impact, and ethical considerations. Journal of Education and Learning Reviews, 3(2), Article e2719. https://doi.org/10.60027/jelr.2026.e2719
Issa, T., & Hall, M. (2024). A teamwork framework for preventing breaches of academic integrity and improving students’ collaborative skills in the AI era. Heliyon, 10(19), Article e38759. https://doi.org/10.1016/j.heliyon.2024.e38759
Jayasinghe, S., Gamage, K. A. A., Yang, D., Cheng, C., Disanayake, C., & Apeji, U. D. (2026). Six institutional intervention areas to support ethical and effective student use of generative AI in higher education: A narrative review. Education Sciences, 16(1), Article 137. https://doi.org/10.3390/educsci16010137
Kemp, S. (2025, March 3). Digital 2025: Bhutan. DataReportal. https://datareportal.com/reports/digital-2025-bhutan
Koscielniak, M., Chudzicka-Czupała, A., & Gasiorowska, A. (2026). Intentions for AI academic misconduct across 15 countries: The theory of planned behavior and past misconduct perspective. Scientific Reports. Advance online publication. https://doi.org/10.1038/s41598-026-61543-1
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114–121. https://doi.org/10.1037/0021-9010.86.1.114
Lund, B. D., Lee, T. H., Mannuru, N. R., & Arutla, N. (2025). AI and academic integrity: Exploring student perceptions and implications for higher education. Journal of Academic Ethics, 23(3), 1545–1565. https://doi.org/10.1007/s10805-025-09613-3
McCabe, D. L., Treviño, L. K., & Butterfield, K. D. (2001). Cheating in academic institutions: A decade of research. Ethics & Behavior, 11(3), 219–232. https://doi.org/10.1207/S15327019EB1103_2
McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates.
Mendiola, M. B., Hayag, G., Oling, R. J., Manulat, S., & Naparan, G. (2026). Exploring teachers’ lived experiences in assessing authentic student learning in AI-influenced classrooms: A phenomenological study. Journal of Education and Learning Reviews, 3(3), Article e3069. https://doi.org/10.60027/jelr.2026.e3069
Ogbaga, I., Onwudebelu, U., Akwuma, N., & Igwe, K. (2026). Academic integrity in the era of generative AI: Students’ perceptions, ethical boundaries, and risk-taking behavior in Nigerian universities. Systems and Computing, 2(2), 95–125. https://doi.org/10.64409/sycom.v2.i2.44
Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(6). https://doi.org/10.53761/q3azde36
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879
Rajah-Kanagasabai, C. J., & Roberts, L. D. (2015). Predicting self-reported research misconduct and questionable research practices in university students using an augmented theory of planned behavior. Frontiers in Psychology, 6, Article 535. https://doi.org/10.3389/fpsyg.2015.00535
Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., Iahad, N. A., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., AlHumaid, J., Allam, M. F., Alló, M., Andoh, R. P. K., Andronic, O., Arthur, Y. D., Aydın, F., Badran, A., Balbontín-Alvarado, R., Ben Saad, H., . . . Aristovnik, A. (2025). Higher education students’ perceptions of ChatGPT: A global study of early reactions. PLOS ONE, 20(2), Article e0315011. https://doi.org/10.1371/journal.pone.0315011
Royal University of Bhutan. (n.d.-a). About RUB. Retrieved September 28, 2026, from https://www.rub.edu.bt/about-rub/
Royal University of Bhutan. (n.d.-b). Academics. Retrieved October 1, 2026, from https://www.rub.edu.bt/academics/
Senali, M. G. (2026). Extending the theory of planned behaviour to explain contract cheating in the age of generative AI: Evidence from Australian higher education. Education and Information Technologies, 31, 9387–9424. https://doi.org/10.1007/s10639-026-14145-3
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3–4), 591–611. https://doi.org/10.2307/2333709
Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312. https://doi.org/10.2307/270723
Stone, T. H., Jawahar, I. M., & Kisamore, J. L. (2009). Using the theory of planned behavior and cheating justifications to predict academic misconduct. Career Development International, 14(3), 221–241. https://doi.org/10.1108/13620430910966415
Swanson, B. A. (2026a). Convenience amid crisis: Explaining generative AI misuse and compliance intention among Myanmar university students. Journal of ICT and Education, 1(2), 83–93. https://doi.org/10.69739/jicte.v1i2.2263
Swanson, B. A. (2026b). Safeguarding academic quality in the age of generative AI: Unauthorized AI use, assessment integrity, and institutional quality assurance in Iraqi universities. The Arab Journal for Quality Assurance in Higher Education, 19(70). https://doi.org/10.20428/ajqahe.v19i70.3948
Sykes, G. M., & Matza, D. (1957). Techniques of neutralization: A theory of delinquency. American Sociological Review, 22(6), 664–670. https://doi.org/10.2307/2089195
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
Tamang, T. O., Daker, S., Gurung, B., & Dorji, U. (2026). Students’ perspectives on integrating generative AI tools into teaching, learning, and assessment in higher education: A case of the Royal University of Bhutan. Journal of Research in Education and Pedagogy, 3(2), 130–154. https://doi.org/10.70232/jrep.v3i2.153
Verri, F. (2026). Protecting academic integrity in international schools: Educational leadership, stakeholder pressure, and institutional trust. Journal of Education and Learning Reviews, 3(4), Article e3233. https://doi.org/10.60027/jelr.2026.e3233
Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21, Article 21. https://doi.org/10.1186/s41239-024-00453-6