Exploratory survey on factors influencing the adoption of AI by university students
Parole chiave:
Artificial Intelligence, UTUAT, inclusive educationAbstract
This study explores the use and acceptance levels of Generative Artificial Intelligence in two groups of Bachelor degree students enrolled at the University of Salerno in General Education Sciences and Motor, Sports and Psychomotor Education Sciences. Through an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the investigation examines the influence of a series of constructs that include, among others, Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions on Behavioral Intentions, and the tendency to use AI systems when studying. As highlighted in the report published by Save the Children, a growing percentage of adolescents use AI not only for informational purposes, but also for emotional and decision-making support, signaling an increasingly integrated relationship between young people and algorithmic agents. This data and literature reporting concerns regarding the use of AI and inclusion, calls for a broader peda-gogical debate to rethink learning beyond an isolationist view of the mind and a reflection on how to educate students in the conscious and critical use of AI systems. In this perspective, this study utilizes an extended UTAUT model that is not limited to measuring the acceptance of technology but may contribute to outlining useful guidelines for the construction of a “radically extended” pedagogy, capable of critically and consciously integrating generative AI into contemporary educational practices.
Riferimenti bibliografici
Ainscow, M. (2020). Promoting inclusion and equity in education: Lessons from international experiences. Nordic Journal of Studies in Educational Policy, 6(1), 7–16. https://doi.org/10.1080/20020317.2020.1729587.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Andrews, J. E., Ward, H., & Yoon, J. (2021). UTAUT as a model for understanding intention to adopt AI and related technologies among librarians. The Journal of Academic Librarianship, 47(6), 102437.
Biesta, G. (2015). Good education in an age of measurement. Routledge.
Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International journal of educational technology in higher education, 17(1), 2.
CAST. (2018). Universal Design for Learning guidelines version 2.2. http://udlguidelines.cast.org
CEDEFOP (2021). Glossary: Digital native. European Centre for the Development of Vocational Training.
Ciofalo, G., Pedroni, M., & Setiffi, F. (2024). ChatGPT Goes to Academia. Una ricerca esplorativa su usi e immaginari dell’intelligenza artificiale da parte di studenti e accademici. Sociologia della comu-nicazione, (2023/66).
Di Domenico, M., & Di Tore, P. A. (2025). Intelligenza artificiale e democrazia: sfide etiche per l'educa-zione. Journal of Inclusive Methodology and Technology in Learning and Teaching, 5(1).
Di Tore, P. A., Di Tore, S., & Todino, M. Le mele improbabili: perché l’intelligenza artificiale non sostituirà il docente (e men che meno la scimmia). Journal of Inclusive Methodology and Technology in Learning and Teaching (forthcoming). Retrievable at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4786876
Dreytser, S. I. (2025). Artificial intelligence application for reflection development through the imple-mentation of the orienting basis of reflective action for students of the pedagogical depart-ments. Russian Journal of Education and Psychology, 16(3), 33-55.
Fiorucci, A., & Bevilacqua, A. (2024). Promuovere l’inclusione e la partecipazione sociale delle persone con disabilità attraverso l’intelligenza artificiale.: Un focus sulla disabilità visiva. Medical Humanities & Medicina Narrativa-MHMN, 9(2), 165-181.
Florian, L. (2019). On the necessary co-existence of special and inclusive education. International Journal of Inclusive Education, 23(7–8), 691–704.
Florian, L., & Black-Hawkins, K. (2011). Exploring inclusive pedagogy. Cambridge Journal of Education, 41(4), 813–828. 10.1080/01411926.2010.501096.
Florian, L., & Spratt, J. (2013). Enacting inclusion: A framework for interrogating inclusive practice. Eu-ropean Journal of Special Needs Education, 28(2), 119–135.
Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promise and implications for teaching and learning. Center for Curriculum Redesign.
Kasneci, E., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, https://doi.org/10.1016/j.lindif.2023.102274.
Khechine, H., Raymond, B., & Augier, M. (2020). The adoption of learning technologies in higher educa-tion: A UTAUT perspective. Education and Information Technologies, 25(6), 5311–5332.
Lecce, A., & Di Tore, S. (2025). L’Intelligenza Artificiale a scuola: il potenziale creativo e inclusivo del Machine Learning. ITALIAN JOURNAL OF SPECIAL EDUCATION FOR INCLUSION, 13(1), 311-318.
Mouta A., Pinto-Llorente A. M., and Torrecilla-Sánchez E. M. (2023). Uncovering blind spots in education ethics: Insights from a systematic literature review on artificial intelligence in education. Interna-tional Journal of Artificial Intelligence in Education. DOI: 10.1007/s40593-023-00384-9.
OECD (2025), “What should teachers teach and students learn in a future of powerful AI?”, OECD Education Spotlights, No. 20, OECD Publishing, Paris, https://doi.org/10.1787/ca56c7d6-en
Pace, E. M., & Zappalà, E. (2021). Armonizzare la testa, le mani e il cuore dei futuri educatori/maestri per un agire educativo inclusivo: Un approccio riflessivo. Nuova Secondaria, 9, 371–386.
Pagliara, S. M., Bonavolonta, G., Pia, M., Falchi, S., Zurru, A. L., Fenu, G., & Mura, A. (2024). The inte-gration of artificial intelligence in inclusive education: A scoping review. Information, 15(12), 774.
Pinnelli, S. (2025). Ricerche e riflessioni della Pedagogia Speciale sull’Intelligenza Artificiale. ITALIAN JOURNAL OF SPECIAL EDUCATION FOR INCLUSION, 13, 15-17.
Prensky, M. (2001). Digital natives, digital immigrants. Part. MCB UP Ltd https://doi. org/10.1108/10748120110424843.
Ranieri, M. (2024). Intelligenza artificiale a scuola. Una lettura pedagogico-didattica delle sfide e delle opportunità. Rivista di Scienze dell'Educazione, 62(1).
Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2022). Social isolation and acceptance of AI-based learning tools: Extending UTAUT. Computers & Education, 181, 104466.
Salas-Pilco, S. Z., Xiao, K., & Oshima, J. (2022). Artificial intelligence and new technologies in inclusive education for minority students: A systematic review. Sustainability, 14(20), 13572.
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & education, 128, 13-35.
Selwyn, N. (2016). Education and technology: Key issues and debates. Bloomsbury.
Sibilio, M. (2025). L’etica dell’IA. Una questione complessa e urgente. In Besio, S., Pinnelli, S., Sibilio, M. (2025). Introduzione. Italian Journal of Special Education for Inclusion, 13, 1.
Taiwo, A. A., & Downe, A. G. (2013). The theory of user acceptance and use of technology (UTAUT): A meta-analytic review of empirical findings. Journal of Theoretical & Applied Information Tech-nology, 49(1).
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440.
Teo, T. (2019). Students and technology acceptance: The role of affect. British Journal of Educational Technology, 50(6), 2927–2941.
UNESCO (n.d.). Ethics of Artificial Intelligence. The Recommendation. Retrievable at: https://www.unesco.org/en/artificial-intelligence/recommendation-ethics
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information tech-nology: Toward a unified view. MIS quarterly, 425-478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of tech-nology (UTAUT): a literature review. Journal of enterprise information management, 28(3), 443-488.
Wong, A. T. Y., Ma, W. W. K., & Lee, C. S. (2023). Students’ acceptance of AI-powered learning tools in higher education. Computers & Education: Artificial Intelligence, 4, 100116.
Xue, L., Rashid, A. M., & Ouyang, S. (2024). The unified theory of acceptance and use of technology (UTAUT) in higher education: A systematic review. Sage Open, 14(1), 21582440241229570.
Zappalà, E., Campitiello, L., Bilotti, U., Di Tore, S., & Sibilio, M. (2025). Rethinking IEP design through AI: towards a dialogic ecology of school inclusion. Journal of Inclusive Methodology and Technology in Learning and Teaching, 5(4).
Zawacki-Richter, O., et al. (2019). Systematic review of AI in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0
Zhang K., Aslan A. B. (2021). AI technologies for education: Recent research & future directions. Com-puters and Education Artificial Intelligence, 2, 100025. DOI: 10.1016/j.caeai.2021.100025.
##submission.downloads##
Pubblicato
Come citare
Fascicolo
Sezione
Licenza
Copyright (c) 2026 Emanuela Zappalà, Erika Marie Pace, Stefano Di Tore

Questo lavoro è fornito con la licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 4.0 Internazionale.