Email this article 
Academic Cheating with Generative AI in Higher Education: An Extended Model of the Theory of Planned Behavior with Motivational Antecedents
https://doi.org/10.22146/jpsi.107932 Muhammad Taslim(1*), Riki Purnama Putra(2), Nurussakinah Daulay(3), Sefa Bulut(4)
(1) Early Childhood Islamic Education Study Program, IAIN Fattahul Muluk Papua, Indonesia
(2) Physics Education Study Program, UIN Sunan Gunung Djati Bandung, Indonesia
(3) Islamic Guidance and Counseling Study Program, UIN Sumatera Utara Medan, Indonesia
(4) Departement of Counseling Psychology & Guidance, Ibn Haldun University, Istanbul, Türkiye
(*) Corresponding Author
Abstract
Keywords
Full Text:
PDFReferences
Al-Mamary, Y. H., & Abubakar, A. A. (2025). Empowering ChatGPT adoption in higher education: A comprehensive analysis of university students’ intention to adopt artificial intelligence using self-determination and technology-to-performance chain theories. The Internet and Higher Education, 66, 101015.
Al-Qaysi, N., Al-Emran, M., Al-Sharafi, M. A., Iranmanesh, M., Ahmad, A., & Mahmoud, M. A. (2024). Determinants of ChatGPT use and its impact on learning performance: An integrated model of BRT and TPB. International Journal of Human–Computer Interaction, 1–13.
Alotaibi, S. M. F. (2025). Determinants of Generative Artificial Intelligence (GenAI) adoption among university students and its impact on academic performance: the mediating role of trust in technology. Interactive Learning Environments, 1–30.
Annamalai, N., Bervell, B., Mireku, D. O., & Andoh, R. P. K. (2025). Artificial intelligence in higher education: Modelling students’ motivation for continuous use of ChatGPT based on a modified self-determination theory. Computers and Education: Artificial Intelligence, 8, 100346.
Berendt, B., Littlejohn, A., & Blakemore, M. (2020). AI in education: Learner choice and fundamental rights. Learning, Media and Technology, 45(3), 312–324.
Bettayeb, A. M., Abu Talib, M., Sobhe Altayasinah, A. Z., & Dakalbab, F. (2024). Exploring the impact of ChatGPT: conversational AI in education. Frontiers in Education, 9, 1379796.
Bin-Nashwan, S. A., Sadallah, M., & Bouteraa, M. (2023). Use of ChatGPT in academia: Academic integrity hangs in the balance. Technology in Society, 75, 102370.
Cano, J. R., & Nunez, N. A. (2024). Unlocking innovation: how enjoyment drives GenAI use in higher education. Frontiers in Education, 9, 1483853.
Çelik, Ö., & Razı, S. (2023). Facilitators and barriers to creating a culture of academic integrity at secondary schools: an exploratory case study. International Journal for Educational Integrity, 19(1), 4.
Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10(1), 60.
Chen, A., Jia, J., Li, Y., & Fu, L. (2025). Investigating the Effect of Role-Play Activity With GenAI Agent on EFL Students’ Speaking Performance. Journal of Educational Computing Research, 63(1), 99–125.
Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197.
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.
Cronan, T. P., Mullins, J. K., & Douglas, D. E. (2018). Further understanding factors that explain freshman business students’ academic integrity intention and behavior: Plagiarism and sharing homework. Journal of Business Ethics, 147, 197–220.
Currie, G. M. (2023). Academic integrity and artificial intelligence: is ChatGPT hype, hero or heresy? Seminars in Nuclear Medicine, 53(5), 719–730.
Dai, Y. (2025). Why students use or not use generative AI: Student conceptions, concerns, and implications for engineering education. Digital Engineering, 4, 100019.
Davis, M. (2022). Examining and improving inclusive practice in institutional academic integrity policies, procedures, teaching and support. International Journal for Educational Integrity, 18(1), 14.
Du, J., & Alm, A. (2024). The impact of ChatGPT on English for academic purposes (EAP) students’ language learning experience: A self-determination theory perspective. Education Sciences, 14(7), 726.
Firdaus, J. A., Ummah, R. I., Aprialini, R. R., Fithriyyah, A., Mahsusi, M., & Faizin, A. (2025). Ketergantungan penggunaan kecerdasan buatan (AI) pada tugas akademik mahasiswa terhadap kemampuan berpikir kritis dan kreatif. Didaktika: Jurnal Kependidikan, 14(1 Februari), 1203–1214.
Fošner, A. (2024). University students’ attitudes and perceptions towards ai tools: implications for sustainable educational practices. Sustainability, 16(19), 8668.
Gayathri Devi, S., Manjula Gandhi, S., Sathya, C., Chandia, S., & Vani, K. H. (2023). Maintaining Performance and QoS of Software Tools for Remote Teaching Environment. In Sustainable Blended Learning in STEM Education for Students with Additional Needs (pp. 195–245). Springer.
Glendinning, I. (2022). Aligning academic quality and standards with academic integrity. In Contract cheating in higher education: Global perspectives on theory, practice, and policy (pp. 199–218). Springer.
Gottardello, D., & Karabag, S. F. (2022). Ideal and actual roles of university professors in academic integrity management: a comparative study. Studies in Higher Education, 47(3), 526–544.
Guilherme, A. (2019). AI and education: the importance of teacher and student relations. AI & Society, 34, 47–54.
Hair Jr, Joe F, Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110.
Hair Jr, Joseph F, Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., Ray, S., Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). An introduction to structural equation modeling. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook, 1–29. https://doi.org/10.1007/978-3-030-80519-7_1
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275–285.
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, 115–135.
Hsiao, C.-H., & Tang, K.-Y. (2024). Beyond acceptance: an empirical investigation of technological, ethical, social, and individual determinants of GenAI-supported learning in higher education. Education and Information Technologies, 1–26.
Ivanov, S., Soliman, M., Tuomi, A., Alkathiri, N. A., & Al-Alawi, A. N. (2024). Drivers of generative AI adoption in higher education through the lens of the Theory of Planned Behaviour. Technology in Society, 77, 102521.
Jiao, J., & Cao, X. (2024). Research on designers’ behavioral intention toward Artificial Intelligence-Aided Design: integrating the Theory of Planned Behavior and the Technology Acceptance Model. Frontiers in Psychology, 15, 1450717.
Jordan, P. J., & Troth, A. C. (2020). Common method bias in applied settings: The dilemma of researching in organizations. Australian Journal of Management, 45(1), 3–14.
Julián, M., & Bonavia, T. (2020). Determinants of students’ willingness to engage in corruption in an academic setting: An empirical study. Journal of Academic Ethics, 18(4), 363–375.
Kangwa, D., Msafiri, M. M., & Fute, A. (2025). Balancing innovation and ethics: promote academic integrity through support and effective use of GenAI tools in higher education. AI and Ethics, 1–34.
Khan, Y., Hameed, I., & Akram, U. (2023). What drives attitude, purchase intention and consumer buying behavior toward organic food? A self-determination theory and theory of planned behavior perspective. British Food Journal, 125(7), 2572–2587.
Khern-am-nuai, W., Kannan, K., & Ghasemkhani, H. (2018). Extrinsic versus intrinsic rewards for contributing reviews in an online platform. Information Systems Research, 29(4), 871–892.
Khoiri, A., Komariah, N., Utami, R. T., Paramarta, V., & Sunarsi, D. (2021). 4Cs analysis of 21st century skills-based school areas. Journal of Physics: Conference Series, 1764(1), 12142.
Kock, F., Berbekova, A., & Assaf, A. G. (2021). Understanding and managing the threat of common method bias: Detection, prevention and control. Tourism Management, 86, 104330.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration (Ijec), 11(4), 1–10.
Kock, N., & Dow, K. E. (2025). Statistical significance and effect size tests in SEM: common method bias and strong theorizing. In Advances in Management Accounting (pp. 95–105). Emerald Publishing Limited.
Kotera, Y., Taylor, E., Fido, D., Williams, D., & Tsuda-McCaie, F. (2023). Motivation of UK graduate students in education: Self-compassion moderates pathway from extrinsic motivation to intrinsic motivation. Current Psychology, 42(12), 10163–10176.
Kumar, R., Eaton, S. E., Mindzak, M., & Morrison, R. (2024). Academic integrity and artificial intelligence: An overview. Second Handbook of Academic Integrity, 1583–1596.
Law, K. K., Chan, A., & Ozer, M. (2017). Towards an integrated framework of intrinsic motivators, extrinsic motivators and knowledge sharing. Journal of Knowledge Management, 21(6), 1486–1502.
Liu, Y., Zhang, Z., & Wu, Y. (2025). What drives Chinese university students’ long-term use of GenAI? Evidence from the heuristic-systematic model. Education and Information Technologies, 1–34.
Luo, J. (2025). How does GenAI affect trust in teacher-student relationships? Insights from students’ assessment experiences. Teaching in Higher Education, 30(4), 991–1006.
Luqman, A., Masood, A., & Ali, A. (2018). An SDT and TPB-based integrated approach to explore the role of autonomous and controlled motivations in “SNS discontinuance intention.” Computers in Human Behavior, 85, 298–307.
Ma, Y. (2025). Modelling College Students’ Acceptance to Use Generative Artificial Intelligence for Second Language Learning: A Theory of Planned Behaviour Perspective. European Journal of Education, 60(1), e12923.
Malhotra, Y., Galletta, D. F., & Kirsch, L. J. (2008). How endogenous motivations influence user intentions: Beyond the dichotomy of extrinsic and intrinsic user motivations. Journal of Management Information Systems, 25(1), 267–300.
Mathrani, A., Han, B., Mathrani, S., Jha, M., & Scogings, C. (2021). Interpreting academic integrity transgressions among learning communities. International Journal for Educational Integrity, 17, 1–16.
Monzon, N., & Hays, F. A. (2025). Leveraging Generative Artificial Intelligence to Improve Motivation and Retrieval in Higher Education Learners. JMIR Medical Education, 11(1), e59210.
Mulenga, R., & Shilongo, H. (2024). Academic integrity in higher education: Understanding and addressing plagiarism. Acta Pedagogia Asiana, 3(1), 30–43.
Nathania, C. A., Arta, S., Maufa, J. B. P., Butar, N. C. B., Sefia, Z. U., & Handoyo, E. R. (2023). Analisis User Experience Penggunaan ChatGPT pada Lingkungan Pendidikan Tinggi. KONSTELASI: Konvergensi Teknologi Dan Sistem Informasi, 3(2), 307–316.
Nelson, A. S., Santamaría, P. V, Javens, J. S., & Ricaurte, M. (2025). Students’ Perceptions of Generative Artificial Intelligence (GenAI) Use in Academic Writing in English as a Foreign Language. Education Sciences, 15(5), 611.
Nikolic, S., Wentworth, I., Sheridan, L., Moss, S., Duursma, E., Jones, R. A., Ros, M., & Middleton, R. (2024). A systematic literature review of attitudes, intentions and behaviours of teaching academics pertaining to AI and generative AI (GenAI) in higher education: An analysis of GenAI adoption using the UTAUT framework. Australasian Journal of Educational Technology.
Oviedo-Trespalacios, O., Peden, A. E., Cole-Hunter, T., Costantini, A., Haghani, M., Rod, J. E., Kelly, S., Torkamaan, H., Tariq, A., & Newton, J. D. A. (2023). The risks of using ChatGPT to obtain common safety-related information and advice. Safety Science, 167, 106244.
Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P., & Sra, M. (2021). AI-generated characters for supporting personalized learning and well-being. Nature Machine Intelligence, 3(12), 1013–1022.
Perkins, M. (2023). Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching and Learning Practice, 20(2), 1–24.
Rasul, T., Nair, S., Kalendra, D., Balaji, M. S., de Oliveira Santini, F., Ladeira, W. J., Rather, R. A., Yasin, N., Rodriguez, R. V, & Kokkalis, P. (2024). Enhancing academic integrity among students in GenAI Era: A holistic framework. The International Journal of Management Education, 22(3), 101041.
Renfeng, J., Gang, Y., & Qi, S. (2025). The Motivational Impact of GenAI Tools in Language Learning: a Quasi‐Experiment Study. International Journal of Applied Linguistics.
Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS Release: 4. SmartPLS GmbH. https://www.smartpls.com
Rodrigues, M., Silva, R., Borges, A. P., Franco, M., & Oliveira, C. (2025). Artificial intelligence: Threat or asset to academic integrity? A bibliometric analysis. Kybernetes, 54(5), 2939–2970.
Roe, J., & Perkins, M. (2022). What are Automated Paraphrasing Tools and how do we address them? A review of a growing threat to academic integrity. International Journal for Educational Integrity, 18(1), 15.
Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1), 342–363.
Sallam, M., Elsayed, W., Al-Shorbagy, M., Barakat, M., El Khatib, S., Ghach, W., Alwan, N., Hallit, S., & Malaeb, D. (2024). ChatGPT usage and attitudes are driven by perceptions of usefulness, ease of use, risks, and psycho-social impact: a study among university students in the UAE. Frontiers in Education, 9, 1414758.
Schiff, D. (2022). Education for AI, not AI for education: The role of education and ethics in national AI policy strategies. International Journal of Artificial Intelligence in Education, 32(3), 527–563.
Shoufan, A. (2023). Exploring students’ perceptions of ChatGPT: Thematic analysis and follow-up survey. IEEE Access, 11, 38805–38818.
Sivasubramaniam, S. (2025). Deterring Academic Integrity Breaches: The Roles of Institutions, Academics, and Support Services. Journal of Scholarly Publishing, 56(2), 240–268.
Stone, A. (2023). Student perceptions of academic integrity: a qualitative study of understanding, consequences, and impact. Journal of Academic Ethics, 21(3), 357–375.
Surahman, E., & Wang, T. (2022). Academic dishonesty and trustworthy assessment in online learning: A systematic literature review. Journal of Computer Assisted Learning, 38(6), 1535–1553.
Susnjak, T. (2023). Beyond predictive learning analytics modelling and onto explainable artificial intelligence with prescriptive analytics and ChatGPT. International Journal of Artificial Intelligence in Education, 1–31.
Szymkowiak, A., Melović, B., Dabić, M., Jeganathan, K., & Kundi, G. S. (2021). Information technology and Gen Z: The role of teachers, the internet, and technology in the education of young people. Technology in Society, 65, 101565.
Tzirides, A. O. O., Zapata, G., Kastania, N. P., Saini, A. K., Castro, V., Ismael, S. A., You, Y., dos Santos, T. A., Searsmith, D., & O’Brien, C. (2024). Combining human and artificial intelligence for enhanced AI literacy in higher education. Computers and Education Open, 6, 100184.
Wang, K., Lin, L., Abdallah, M., & Cai, W. (2025). Where is the Boundary? Understanding How People Recognize and Evaluate Generative AI-extended Videos. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1–19.
Wang, N., Wang, X., & Su, Y.-S. (2024). Critical analysis of the technological affordances, challenges and future directions of Generative AI in education: A systematic review. Asia Pacific Journal of Education, 44(1), 139–155.
Whalen, J., & Mouza, C. (2023). ChatGPT: Challenges, opportunities, and implications for teacher education. Contemporary Issues in Technology and Teacher Education, 23(1), 1–23.
Wu, Q., Tian, J., & Liu, Z. (2025). Exploring the usage behavior of generative artificial intelligence: a case study of ChatGPT with insights into the moderating effects of habit and personal innovativeness. Current Psychology, 1–14.
Yeo, M. A. (2023). Academic integrity in the age of Artificial Intelligence (AI) authoring apps. Tesol Journal, 14(3), e716.
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(1), 21.
Zlotnikova, I., Hlomani, H., Mokgetse, T., & Bagai, K. (2025). Establishing ethical standards for GenAI in university education: a roadmap for academic integrity and fairness. Journal of Information, Communication and Ethics in Society. DOI: https://doi.org/10.22146/jpsi.107932
Article Metrics
Abstract views : 3869
| 
views : 1232
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Jurnal Psikologi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Recent Issues
 | | | | ||||
| Vol 51 Issue 2 (2024) Page 120-230 | Vol 51 Issue 1 (2024) Page 1-119 | Vol 50 Issue 3 (2023) Page 219-332 | Vol 50 Issue 2 (2023) Page 125-218 |
| Published by Faculty of Psychology, Universitas Gadjah Mada, Indonesia Building D-6th, Floor No. D-606. Jl. Sosio Humaniora No. 1, Bulaksumur Yogyakarta, 55281 Email: jurnalpsikologi@ugm.ac.id Phone/whatsApp: +6281125210175 |
