Typology of Higher Education Faculty Attitudes Toward Artificial Intelligence: Results of Cluster Analysis

Abstract

Abstract

Background. The accelerated implementation of artificial intelligence (AI) in higher education is changing the content of faculty professional activity, while faculty attitudes toward AI largely shape universities' digital transformation. However, the structure of these attitudes and their typological diversity remain insufficiently explored. Most existing studies rely either on general technology acceptance models or on isolated opinions and concerns, whereas typological reconstruction of stable psychological profiles remains underdeveloped.

Objective. To identify and describe a typology of higher education faculty attitudes toward the use of AI in their professional activity.

Study Participants. The study involved 400 faculty members from several Russian universities representing different institutional profiles. The mean age was 38.07 years, and the mean teaching experience was 11.65 years.

Methods. The study employed six scales of the validated author-developed questionnaire "Teachers' Attitudes toward Artificial Intelligence," along with indicators of AI use intensity, AI-related behavioral practices, organizational context, and work engagement. The typology of attitudes was identified using hierarchical cluster analysis and k-means clustering. Differences between the clusters were assessed through parametric and nonparametric statistical procedures.

Results. The study has identified three types of teachers' attitudes toward AI: "pragmatically accepting," "normative-reflective users," and "anxiously distanced skeptics." The strongest contributions to the differentiation of these types were made by perceived usefulness of AI, readiness for its implementation, subjective competence, and professional anxiety. Normative-regulatory indicators showed weaker discriminative power. Age, teaching experience, and work engagement were not statistically significantly associated with the typology. Differences in AI use frequency were limited, whereas differences in AI-related practices and organizational context were more pronounced.

Conclusions. Faculty attitudes toward AI represent a multidimensional psychological construct that includes resource-related, anxiety-related, and normative-regulatory dimensions. The identified typology allows moving beyond an averaged description of AI acceptance and reconstructing qualitatively distinct psychological profiles, which may inform professional development programs, digital adaptation strategies, and institutional AI policies in higher education.

References

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

Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Dwivedi, Y.K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C. et al. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, (57). URL: https://www.sciencedirect.com/science/article/abs/pii/S026840121930917X?via%3Dihub (access date: 02.06.2025).

Grigoriev, S.M. (2025). Development and validation of the questionnaire "Teachers' attitudes toward artificial intelligence". Sistemnaya psikhologiya i sotsiologiya = Systems Psychology and Sociology, 56(4), 15–26. (In Russ.). https://doi.org/10.24412/2223-6872-2025-456-15-26

Karpenko, O.M. (2020). The role and functions of a teacher in a higher education organization in the context of digitalization of education. Chelovecheskiy kapital = Human Capital, 4(136), 18–23. (In Russ.)

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D. et al. (2023). ChatGPT for Good? On opportunities and challenges of large language models for education. Learning and Individual Differences, (103), 102274. URL: https://www.sciencedirect.com/science/article/abs/pii/S1041608023000195?via%3Dihub (access date: 10.07.2025).

Knox, J., Williamson, B., Bayne, S. (2019). Machine behaviourism: future visions of 'learnification' and 'datafication' across humans and digital technologies. Learning, Media and Technology, (45), 1–15. https://doi.org/10.1080/17439884.2019.1623251

Krstić, L., Aleksić, V., Krstić, M. (2022). Artificial Intelligence in Education: A Review. In A. Veljko (ed.), 9th International Scientific Conference Technics and Informatics in Education (September 16–18, 2022) (pp. 223–228). Čačak: University of Kragujevac Publ.

Miao, F., Holmes, W. (2024). Handbook of Generative Artificial Intelligence in Education and Research. Paris: UNESCO. (In Russ.). https://doi.org/10.54675/EWZM9535

Nevárez Montes, J., Elizondo-Garcia, J. (2025). Faculty acceptance and use of generative artificial intelligence in their practice. Frontiers in Education, (10). URL: https://www.sciencedirect.com/science/article/abs/pii/S026840121930917X?via%3Dihub (access date: 03.04.2025).

Pospelova, E.A., Ototsky, P.L., Gorlacheva, E.N., Fayzullin, R.V. (2024). Generative artificial intelligence in education: current trends and prospects. Professionalnoye obrazovaniye i rynok truda = Professional Education and Labor Market, 12(3), 6–21. (In Russ.). https://doi.org/10.52944/PORT.2024.58.3.001

Robert, I.V. (2024). Realizing the potential of artificial intelligence in education. Prostranstvo pedagogicheskikh issledovaniy = Pedagogical Research Space, 1(1), 60–75. (In Russ.). https://doi.org/10.23859/3034-1760.2024.77.66.004

Robert, I.V. (2025). Development of informatization of education in the period of digital transformation. Pedagogicheskoye obrazovaniye i nauka = Pedagogical Education and Science, (2), 7–11. (In Russ.). https://doi.org/10.56163/2072-2524-2025-2-7-11

Rudenko, E.S., Turyanskaya, S.A. (2025). Generative artificial intelligence for teachers: strategies, tools, and ethics. Pedagogicheskaya perspektiva = Pedagogical Perspective, 19(3), 20–32. (In Russ.). https://doi.org/10.55523/27822559_2025_3(19)_20

Schepman, A., Rodway, P. (2020). Initial Validation of the General Attitudes Towards Artificial Intelligence Scale. Computers in Human Behavior Reports, (1), 1–13. https://doi.org/10.1016/j.chbr.2020.100014

Schaufeli, W.B., Bakker, A.B. (2003). UWES — Utrecht Work Engagement Scale: Test manual. Utrecht: Utrecht University Publ.

Selwyn, N. (2021). Education and Technology: Key Issues and Debates (3rd ed.). London: Bloomsbury Academic.

Tlili, A., Shehata, B., Adarkwah, V.A., Bozkurt, A. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, (10), 1–24. https://doi.org/10.1186/s40561-023-00237-x

Trofimova, N.N. (2023). Current problems and main advantages of digitalization of education. Finansovyy biznes = Financial Business, 235(1), 65–67. (In Russ.)

Uznadze, D.N. (2001). Psychology of attitude. St. Petersburg: Piter. (In Russ.)

Xue, L., Rashid, A., Ouyang, S. (2024). The unified theory of acceptance and use of technology (UTAUT) in higher education: A systematic review. SAGE Open, 14(1). URL: https://journals.sagepub.com/doi/10.1177/21582440241229570 (access date: 03.10.2025).

Zawacki-Richter, O., Marín, V.I., Bond, M., Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education — where are the educators? International Journal of Educational Technology in Higher Education, (16), 39–66. https://doi.org/10.1186/s41239-019-0171-0