From Innovation to Imitation: Associations Between Exposure to AI Prompt-Sharing Content on Social Media, Moral Disengagement, and Intention to Engage in Academic Misconduct
Keywords:
AI prompt-sharing; academic misconduct; moral disengagement; social mediaAbstract
The use of AI today has raised significant ethical concerns regarding its potential misuse in academic contexts. This paper aims to examine relationship between university students’ exposure to AI prompt-sharing content on social media, moral disengagement, perceived academic norms, and intentions to engage in AI-assisted academic misconduct. Specifically, the study investigated whether exposure to AI prompt-sharing content was associated with students’ intentions to engage in AI-assisted academic misconduct, whether moral disengagement mediated this relationship, and whether perceived academic norms moderated the association between exposure and moral disengagement. The study conducted SPSS-based descriptive statistics, regression, mediation, and moderation analyses of data collected through a structured questionnaire administered to 600 Chinese university students to verify three hypotheses. The findings indicate that greater exposure to AI prompt-sharing content was significantly associated with stronger intentions to engage in AI-assisted academic misconduct, with moral disengagement acting as a significant mediator, and perceived academic norms strengthening the association between exposure and moral disengagement. These observations indicate that AI prompt-sharing content on social media can be associated with students’ ethical decision-making regarding AI use in academic settings and highlight the importance of promoting responsible AI use and academic integrity in higher education.
https://doi.org/10.26803/ijlter.25.8.25
References
Ahmad, Z., Soroya, M. S. & Awais, A. (2025). Understanding academic integrity beliefs, motivations and behaviors amongst postgraduate students: A comparative analysis of public and private sector universities in Punjab, Pakistan. Journal of Academic Ethics, 24(1), Article 22. https://doi.org/10.1007/s10805-025-09697-x
Alalwan, A. A., Rana, N., Dwivedi, K. Y., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics, 34(7), 1177–1190. https://doi.org/10.1016/j.tele.2017.05.008
Amigud, A., & Pell, D. J. (2022). Virtue, utility and improvisation: A multinational survey of academic staff solving integrity dilemmas. Journal of Academic Ethics, 20(3), 311–333. https://doi.org/10.1007/s10805-021-09416-2
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79–95, https://doi.org/10.1007/s11747-019-00695-1
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26. https://doi.org/10.1146/annurev.psych.52.1.1
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
Eager, B., & Brunton, R. (2023). Prompting higher education towards AI-augmented teaching and learning practice. Journal of University Teaching and Learning Practice, 20(5), 1–19. https://doi.org/10.53761/1.20.5.02
Elom, C. O., Ayanwale, M. A., Ukeje, I. O., Offiah, G. A., Umoke, C. C., & Ogbonnaya, C. E. (2025). Does AI knowledge encourage cheating? Investigating student perceptions, ethical engagement, and academic integrity in the digital age. International Journal of Learning, Teaching and Educational Research, 24(4), 708–729. https://doi.org/10.26803/ijlter.24.4.33
Farajnezhad, S., Bodaghi Khajeh Noubar, H., & Fakhimi Azar, S. (2021). The impact of diffusion of innovation models on user behavioral intention in adopting social media marketing. International Journal of Nonlinear Analysis and Applications, 12(2), 1611–1632. http://dx.doi.org/10.22075/IJNAA.2021.5291
García-Avilés, J. A. (2020). Diffusion of innovation. In J. van den Bulck, D. R. Ewoldsen, M. L. Mares, & E. Scharrer (Eds.), The International Encyclopedia of Media Psychology (pp. 1–8). John Wiley & Sons.
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
Golzar, J., Noor, S., & Tajik, O. (2022). Convenience sampling. International Journal of Education & Language Studies, 1(2), 72–77. https://doi.org/10.22034/ijels.2022.162981
Hamed, S. M. S. H. (2023). The impact of social media on linguistic practices and cultural norms. Journal of Arts, Literature, Humanities and Social Sciences, 95, 276–295. https://doi.org/10.33193/jalhss.95.2023.898
Head, A. J., Fister, B., & MacMillan, M. (2020). Information literacy in the age of algorithms: Student Experiences with news and information, and the need for change. Project Information Literacy.
Kaiser, H.F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/BF02291575
Kotler, P., Keller, K. L., & Chernev, A. (2022). Marketing management. Pearson.
Lasser, J., & Poechhacker, N. (2025). Designing social media content recommendation algorithms for societal good. Annals of the New York Academy of Sciences, 1548(1), 20–28. https://doi.org/10.1111/nyas.15359
Lee, K. (2022). Social media marketing for small business. BNG Books.
Narayanan, A. (2023, March 9). Understanding social media recommendation algorithms. Knight First Amendment Institute at Columbia University. https://knightcolumbia.org/content/understanding-social-media-recommendation-algorithms
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. https://doi.org/10.53761/1.20.02.07
Popescu, R.-I., Sabie, O. M., & Tru?c?, M. I. (2023). The contribution of artificial intelligence to stimulate the innovation of educational services and university programs in public administration. Transylvanian Review of Administrative Sciences, 19(70 E), 85–108. https://doi.org/10.24193/tras.70E.5
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Ross, J. (2017). Speculative method in digital education research. Learning, Media and Technology, 42(2), 214–229. https://doi.org/10.1080/17439884.2016.1160927
Schunk, D. H., & Usher, E. L. (2019). Social cognitive theory and motivation. In R. M. Ryan (Ed.), The Oxford Handbook of Human Motivation (2nd ed., pp. 10–26). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190666453.013.2
Sozon, M., Alkharabsheh, O. H. M., Pok, W. F., Sia, B. C., & Chowdhury, M. A. (2026). Exploring three decades of key themes and trends in academic misconduct in higher education institutions: A literature review. Journal of Applied Research in Higher Education, 18(3), 815–829. https://doi.org/10.1108/JARHE-08-2024-0440
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
Ursava?, Ö. F., Yalç?n, Y., ?slamo?lu, H., Bak?r-Yalç?n, E., & Cukurova, M. (2025). Rethinking the importance of social norms in generative AI adoption: Investigating the acceptance and use of generative AI among higher education students. International Journal of Educational Technology in Higher Education, 22(1), 1–22. https://doi.org/10.1186/s41239-025-00535-z
Wong, S. S. H., Lim, S. W. H., & Quinlan, K. M. (2016). Integrity in and beyond contemporary higher education: What does it mean to university students? Frontiers in Psychology, 7, Article 1094. https://doi.org/10.3389/fpsyg.2016.01094
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