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Trust in Generative AI Tools: A Comparative Study of Higher Education Students, Teachers, and Researchers

Đerić, Elena, Frank, Domagoj, Milković, Marin·2025·Information
DOI 10.3390/info16070622
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Abstract

Generative AI (GenAI) tools, including ChatGPT, Microsoft Copilot, and Google Gemini, are rapidly reshaping higher education by transforming how students, educators, and researchers engage with learning, teaching, and academic work. Despite their growing presence, the adoption of GenAI remains inconsistent, largely due to the absence of universal guidelines and trust-related concerns. This study examines how trust, defined across three key dimensions (accuracy and relevance, privacy protection, and nonmaliciousness), influences the adoption and use of GenAI tools in academic environments. Using survey data from 823 participants across different academic roles, this study employs multiple regression analysis to explore the relationship between trust, user characteristics, and behavioral intention. The results reveal that trust is primarily experience-driven. Frequency of use, duration of use, and self-assessed proficiency significantly predict trust, whereas demographic factors, such as gender and academic role, have no significant influence. Furthermore, trust emerges as a strong predictor of behavioral intention to adopt GenAI tools. These findings reinforce trust calibration theory and extend the UTAUT2 framework to the context of GenAI in education. This study highlights that fostering appropriate trust through transparent policies, privacy safeguards, and practical training is critical for enabling responsible, ethical, and effective integration of GenAI into higher education.

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Research works printed but not linked: 41

  1. Straub, 2009, Understanding Technology Adoption: Theory and Future Directions for Informal Learning, Rev. Educ. Res., 79, 625, 10.3102/0034654308325896 · DOI
  2. Singh, T., Dutta, S., Vyas, S., and Rocha, Á. (2024). Transformative Pedagogy: A Comprehensive Framework for AI Integration in Education. Explainable AI for Education: Recent Trends and Challenges, Springer Nature Switzerland.
  3. Mehrotra, 2024, A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction: Trends, Opportunities and Challenges, ACM J. Responsib. Comput., 1, 1, 10.1145/3696449 · DOI
  4. Gentile, M., Città, G., Perna, S., and Allegra, M. (2023). Do we still need teachers? Navigating the paradigm shift of the teacher’s role in the AI era. Front. Educ., 8. · DOI
  5. Lytras, 2024, Guest editorial: Active and transformative learning in higher education in times of artificial intelligence and ChatGPT, Interact. Technol. Smart Educ., 21, 489, 10.1108/ITSE-11-2024-252 · DOI
  6. Chaudhry, 2022, Artificial Intelligence in Education (AIEd): A high-level academic and industry note 2021, AI Ethics, 2, 157, 10.1007/s43681-021-00074-z · DOI · Paper page
  7. Mittal, 2024, A Comprehensive Review on Generative AI for Education, IEEE Access, 12, 142733, 10.1109/ACCESS.2024.3468368 · DOI · Paper page
  8. Abdelghani, 2024, GPT-3-driven pedagogical agents for training children’s curious question-asking skills, Int. J. Artif. Intell. Educ., 34, 483, 10.1007/s40593-023-00340-7 · DOI · Paper page
  9. Lee, H., and Hwang, Y. (2022). Technology-Enhanced Education through VR-Making and Metaverse-Linking to Foster Teacher Readiness and Sustainable Learning. Sustainability, 14. · DOI · Paper page
  10. Kurni, M., Mohammed, M.S., and Srinivasa, K.G. (2023). AI-Enabled Gamification in Education. A Beginner’s Guide to Introduce Artificial Intelligence in Teaching and Learning, Springer International Publishing. · DOI
  11. Mohamed, 2024, The Impact of Artificial Intelligence on Language Translation: A Review, IEEE Access, 12, 25553, 10.1109/ACCESS.2024.3366802 · DOI
  12. Ferrara, 2024, The Butterfly Effect in artificial intelligence systems: Implications for AI bias and fairness, Mach. Learn. Appl., 15, 100525
  13. Hunkenschroer, 2022, Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda, J. Bus. Ethics, 178, 977, 10.1007/s10551-022-05049-6 · DOI · Paper page
  14. Abdelhalim, 2024, A framework of diversity, equity, and inclusion safeguards for chatbots, Bus. Horiz., 67, 487, 10.1016/j.bushor.2024.03.003 · DOI
  15. Gupta, A., Royer, A., Wright, C., Khan, F.A., Heath, V., Galinkin, E., Khurana, R., Ganapini, M.B., Fancy, M., and Sweidan, M. (2021). The State of AI Ethics Report (January 2021). arXiv.
  16. Frank, D., Bernik, A., and Milković, M. (2024, January 4–6). Efficient Generative AI-Assisted Academic Research: Considerations for a Research Model Proposal. Proceedings of the 2024 IEEE 11th International Conference on Computational Cybernetics and Cyber-Medical Systems (ICCC), Hanoi, Vietnam. · DOI
  17. Liu, 2025, “Turning right”? An experimental study on the political value shift in large language models, Humanit. Soc. Sci. Commun., 12, 179, 10.1057/s41599-025-04465-z · DOI
  18. Buolamwini, J., and Gebru, T. (2025, January 12). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Presented at the FAT, New York, NYC, USA, 23–24 February 2018. Available online: https://www.semanticscholar.org/paper/Gender-Shades%3A-Intersectional-Accuracy-Disparities-Buolamwini-Gebru/18858cc936947fc96b5c06bbe3c6c2faa5614540.
  19. Roy, S.S., Thota, P., Naragam, K.V., and Nilizadeh, S. (2024, January 20–22). From Chatbots to Phishbots?: Phishing Scam Generation in Commercial Large Language Models. Proceedings of the IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA. · DOI
  20. Jacovi, A., Marasović, A., Miller, T., and Goldberg, Y. (2021, January 3–10). Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, in FAccT ’21, Virtually. · DOI · Paper page
  21. Rasmussen, D., and Karlsen, T. (2023). Adopt or Abort? Mapping Students’ and Professors’ Attitudes Towards the Use of Generative AI in Higher Education. [Master’s Thesis, Norwegian School of Economics]. Available online: https://openaccess.nhh.no/nhh-xmlui/handle/11250/3130093.
  22. Christianson, 2024, End the AI detection arms race, Patterns, 5, 101058, 10.1016/j.patter.2024.101058 · DOI
  23. Jabotinsky, H.Y., and Sarel, R. (2023). Co-Authoring with an AI? Ethical Dilemmas and Artificial Intelligence, Social Science Research Network. · DOI
  24. Williamson, 2023, Re-examining AI, automation and datafication in education, Learn. Media Technol., 48, 1, 10.1080/17439884.2023.2167830 · DOI
  25. Huang, 2023, Ethics of Artificial Intelligence in Education: Student Privacy and Data Protection, Sci. Insights Educ. Front., 16, 2577, 10.15354/sief.23.re202 · DOI · Paper page
  26. Strowel, 2023, ChatGPT and Generative AI Tools: Theft of Intellectual Labor?, Institution’s Innov. Counc., 54, 491
  27. Akter, 2023, Advancing algorithmic bias management capabilities in AI-driven marketing analytics research, Ind. Mark. Manag., 114, 243, 10.1016/j.indmarman.2023.08.013 · DOI
  28. Pawelec, 2024, Decent deepfakes? Professional deepfake developers’ ethical considerations and their governance potential, AI Ethics, 5, 2641, 10.1007/s43681-024-00542-2 · DOI
  29. Etienne, 2021, The future of online trust (and why Deepfake is advancing it), AI Ethics, 1, 553, 10.1007/s43681-021-00072-1 · DOI · Paper page
  30. Vaza, 2024, Security And Privacy Concerns In AI-Enabled Iot Educational Frameworks: An In-Depth Analysis, Educ. Adm. Theory Pract., 30, 8436
  31. Venkatesh, 2012, Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology, MIS Q., 36, 157, 10.2307/41410412 · DOI · Paper page
  32. Giunta, C. (2017). An Emerging Awareness of Generation Z Students for Higher Education Professors. Am. Bus. Rev., 5. · DOI
  33. 2020, Educational experiences with Generation Z, Int. J. Interact. Des. Manuf., 14, 847, 10.1007/s12008-020-00674-9 · DOI · Paper page
  34. Otis, N.G., Cranney, K., Delecourt, S., and Koning, R. (2024). Global Evidence on Gender Gaps and Generative AI, Harvard Business School. · DOI
  35. Carvajal, D., Franco, C., and Isaksson, S. (2022). Will Artificial Intelligence Get in the Way of Achieving Gender Equality?, Social Science Research Network. · DOI
  36. Møgelvang, A., Bjelland, C., Grassini, S., and Ludvigsen, K. (2024). Gender Differences in the Use of Generative Artificial Intelligence Chatbots in Higher Education: Characteristics and Consequences. Educ. Sci., 14. · DOI
  37. Nazaretsky, T., Cukurova, M., and Alexandron, G. (2022, January 21–25). An Instrument for Measuring Teachers’ Trust in AI-Based Educational Technology. Proceedings of the LAK22: 12th International Learning Analytics and Knowledge Conference, Online. · DOI · Paper page
  38. Biswas, M., and Murray, J. (2024). The Influence of Education and Self-Perceived Tech Savviness on AI Reliance: The Role of Trust. TrustWorld Congress in Computer Science, Computer Engineering & Applied Computing, Springer Nature Switzerland. · DOI
  39. Falebita, 2024, Artificial Intelligence Tools Usage: A Structural Equation Modeling of Undergraduates’ Technological Readiness, Self-Efficacy and Attitudes, J. STEM Educ. Res., 8, 257, 10.1007/s41979-024-00132-1 · DOI
  40. Nazaretsky, 2025, The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptions, Comput. Educ. Artif. Intell., 8, 100368, 10.1016/j.caeai.2025.100368 · DOI
  41. Bach, 2024, A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective, Int. J. Hum. Comput. Interact., 40, 1251, 10.1080/10447318.2022.2138826 · DOI

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Metadata

Authors
Đerić, E., Frank, D., & Milković, M.
Affiliations
  • Information and Communications Science—Postgraduate Program, University North, 48000 Koprivnica, Croatia
  • Department of Computer Science and Informatics, University North, 48000 Koprivnica, Croatia
  • Department of Multimedia, University North, 48000 Koprivnica, Croatia
ORCID
Venue
Information
Publisher
MDPI AG
Published
2025-07-21
Volume
16
Issue
7
Pages
622
ISSN
2078-2489
DOI
10.3390/info16070622
Cited by
60
References
18
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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