The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Trust in Generative AI Tools: A Comparative Study of Higher Education Students, Teachers, and Researchers
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“This resonates with broader adoption research: Đerić et al. (2025) reported that trust predicts intention to use generative AI tools in education.”
From Authorship Attribution and Trust in AI-Generated Scientific Texts: An Experimental Study (2026) · cites this paper as Đerić et al., 2025
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
What is AI Literacy? Competencies and Design Considerations
Long, D., & Magerko, B. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376727 · Cited by 3,241
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817
Artificial hallucinations in ChatGPT: implications in scientific writing
Alkaissi, H., & McFarlane, S. I. (2023) · Cureus · doi:10.7759/cureus.35179 · Cited by 1,540
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261
Ethical principles for artificial intelligence in education
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11316-w · Cited by 1,228
Wang, B., Rau, P.-L. P., & Yuan, T. (2023) · Behaviour & Information Technology · doi:10.1080/0144929x.2022.2072768 · Cited by 1,036
Trust in AI and Its Role in the Acceptance of AI Technologies
Choung, H., David, P., & Ross, A. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2050543 · Cited by 963
Chan, C. K. Y., & Lee, K. K. W. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00269-3 · Cited by 634
Research works printed but not linked: 41
- Straub, 2009, Understanding Technology Adoption: Theory and Future Directions for Informal Learning, Rev. Educ. Res., 79, 625, 10.3102/0034654308325896 · DOI
- 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.
- 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
- 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
- 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
- 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
- Mittal, 2024, A Comprehensive Review on Generative AI for Education, IEEE Access, 12, 142733, 10.1109/ACCESS.2024.3468368 · DOI · Paper page
- 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
- 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
- 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
- Mohamed, 2024, The Impact of Artificial Intelligence on Language Translation: A Review, IEEE Access, 12, 25553, 10.1109/ACCESS.2024.3366802 · DOI
- Ferrara, 2024, The Butterfly Effect in artificial intelligence systems: Implications for AI bias and fairness, Mach. Learn. Appl., 15, 100525
- 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
- Abdelhalim, 2024, A framework of diversity, equity, and inclusion safeguards for chatbots, Bus. Horiz., 67, 487, 10.1016/j.bushor.2024.03.003 · DOI
- 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.
- 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
- 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
- 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.
- 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
- 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
- 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.
- Christianson, 2024, End the AI detection arms race, Patterns, 5, 101058, 10.1016/j.patter.2024.101058 · DOI
- Jabotinsky, H.Y., and Sarel, R. (2023). Co-Authoring with an AI? Ethical Dilemmas and Artificial Intelligence, Social Science Research Network. · DOI
- Williamson, 2023, Re-examining AI, automation and datafication in education, Learn. Media Technol., 48, 1, 10.1080/17439884.2023.2167830 · DOI
- 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
- Strowel, 2023, ChatGPT and Generative AI Tools: Theft of Intellectual Labor?, Institution’s Innov. Counc., 54, 491
- 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
- Pawelec, 2024, Decent deepfakes? Professional deepfake developers’ ethical considerations and their governance potential, AI Ethics, 5, 2641, 10.1007/s43681-024-00542-2 · DOI
- 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
- Vaza, 2024, Security And Privacy Concerns In AI-Enabled Iot Educational Frameworks: An In-Depth Analysis, Educ. Adm. Theory Pract., 30, 8436
- 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
- Giunta, C. (2017). An Emerging Awareness of Generation Z Students for Higher Education Professors. Am. Bus. Rev., 5. · DOI
- 2020, Educational experiences with Generation Z, Int. J. Interact. Des. Manuf., 14, 847, 10.1007/s12008-020-00674-9 · DOI · Paper page
- Otis, N.G., Cranney, K., Delecourt, S., and Koning, R. (2024). Global Evidence on Gender Gaps and Generative AI, Harvard Business School. · DOI
- Carvajal, D., Franco, C., and Isaksson, S. (2022). Will Artificial Intelligence Get in the Way of Achieving Gender Equality?, Social Science Research Network. · DOI
- 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
- 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
- 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
- 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
- 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
- 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
Cited by
Sorted by citations
Trust in online information as a predictor of misinformation vulnerability among university students
Shah, Z. U. A., Channar, W. A., Hashmi, D. F. S., & Javed, B. (2026) · The Critical Review of Social Sciences Studies · doi:10.59075/bzbttd49 · Cited by 1
Authorship Attribution and Trust in AI-Generated Scientific Texts: An Experimental Study
Matveev, M., & Matveeva, E. (2026) · Психология. Журнал Высшей школы экономики · doi:10.17323/1813-8918-2026-2-253-262
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
- Venue
- Information
- Publisher
- MDPI AG
- Published
- 2025-07-21
- Volume
- 16
- Issue
- 7
- Pages
- 622
- ISSN
- 2078-2489
- Cited by
- 60
- References
- 18
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review