Beyond technical expertise: understanding the relationship between teachers and Artificial Intelligence through the ARISE-AI framework
Abstract
he spread of generative AI in education is redefining how knowledge is produced, mediated and validated, raising questions about teachers’ interaction with these technologies. Against this background, the CREDDI group at eCampus University developed the ARISE-AI framework, a human-centred theoretical model for analysing teacher-AI relationships in socio-educational and training contexts.
Based on questionnaire data from over 5,000 curricular and support teachers, this study examines AI growth potential by comparing current and expected future adoption. Findings show that the greatest potential for intervention lies among teachers with low to medium-low current use, where growth margins are highest. Overall, increasing AI adoption requires skills development and actions strengthening perceived usefulness, trust, motivation, and readiness to integrate AI into teaching and inclusive practices.
Keywords
References
Accoto, C. (2024). Il pianeta latente: Provocazioni della tecnica, innovazioni della cultura. Egea.
Adamoli, M., Emanuel, F., Marangi, M., & Rondonotti, M. (2026a). ARISE-AI: Rilevare e orientare le pratiche didattiche con l’intelligenza artificiale in una prospettiva human-centered. Research Trends in Humanities Education & Philosophy, 13(1), 35–50.
Adamoli, M., Emanuel, F., Marangi, M., Rondonotti, M., & Raviolo, P. (2025a). TEACH-AI: Transformative educational approaches for civic and human-centered AI. In HELMeTO 2025 International Conference: Higher Education Learning Methodologies and Technologies Online—Book of abstracts (pp. 105–107). Studium.
Adamoli, M., Emanuel, F., Marangi, M., Rondonotti, M., & Raviolo, P. (2026b). AI in education: Evidence from cluster-based profiling of social educators and teachers in Italy. Italian Journal of Educational Technology. Advance online publication. https://doi.org/10.17471/2499-4324/1589
Adamoli, M., Marangi, M., Rondonotti, M., & Raviolo, P. (2025b). AI literacy: An explorative analysis of Italian university students’ perceptions of AI skills. Italian Journal of Health Education, Sport and Inclusive Didactics, 9(1).
Adams, V., Murphy, M., & Clarke, A. E. (2009). Anticipation: Technoscience, life, affect, temporality. Subjectivity, 28(1), 246–265.
Al-Zahrani, A. M. (2024). Unveiling the shadows: Beyond the hype of AI in education. Heliyon, 10(9), Article e30696.
Allen, L. K., & Kendeou, P. (2023). ED-AI Lit: An interdisciplinary framework for AI literacy in education. Policy Insights from the Behavioral and Brain Sciences, 11(1), 3–10.
Anand, P., Hunter, G., & Smith, R. (2005). Capabilities and well-being: Evidence based on the Sen-Nussbaum approach to welfare. Social Indicators Research, 74(1), 9–55.
Anand, P., Jones, S., Donoghue, M., & Tietler, J. (2021). Non-monetary poverty and deprivation: A capability approach. Journal of European Social Policy, 31(1), 78–91.
Artyukhov, A., Wołowiec, T., Artyukhova, N., & Bogacki, S. (2024). SDG 4, academic integrity and artificial intelligence: Clash or win-win cooperation? Sustainability, 16, Article 8483.
Boetto, H. (2025). Artificial intelligence in social work: An EPIC model for practice. Australian Social Work, 1–14.
Buccella, A. (2023). “AI for all” is a matter of social justice. AI and Ethics, 3(4), 1143–1152.
Carter, L., Liu, D., & Cantrell, C. (2020). Exploring the intersection of the digital divide and artificial intelligence: A hermeneutic literature review. AIS Transactions on Human-Computer Interaction, 12(4), 253–275.
Costa, E. (2018). Affordances-in-practice: An ethnographic critique of social media logic and context collapse. New Media & Society, 20(10), 3641–3656.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.
Dunleavy, P., & Margetts, H. (2025). Data science, artificial intelligence and the third wave of digital era governance. Public Policy and Administration, 40(2), 185–214.
Ehlert, M., Grunschel, C., & Koehler, F. (2025). The role of teachers’ emotions and their assessment in professional development research: A systematic review. Educational Psychology Review, 37(3), Article 69.
Garkisch, M., & Goldkind, L. (2025). Considering a unified model of artificial intelligence enhanced social work: A systematic review. Journal of Human Rights and Social Work, 10(1), 23–42.
Gibson, J. J. (1979). The ecological approach to visual perception. Houghton Mifflin.
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1), 100–108.
Hodgson, D., Goldingay, S., Boddy, J., Nipperess, S., & Watts, L. (2022). Problematising artificial intelligence in social work education: Challenges, issues and possibilities. The British Journal of Social Work, 52(4), 1878–1895.
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526.
Ketchen, D. J., & Shook, C. L. (1996). The application of cluster analysis in strategic management research: An analysis and critique. Strategic Management Journal, 17(6), 441–458.
Kitsara, I. (2022). Artificial intelligence and the digital divide: From an innovation perspective. In A. Bounfour (Ed.), Platforms and artificial intelligence (pp. 245–265). Springer.
Lamacchia, M., Dicataldo, M. C., Carruba, M. C., & Dipace, A. (2025). Artificial intelligence for inclusion: Teachers’ perceptions. Italian Journal of Special Education for Inclusion, 13(1), 82–95.
Leys, C. (2019). Taking parametric assumptions seriously: Arguments for the use of Welch’s F-test instead of the classical F-test in one-way ANOVA. International Review of Social Psychology, 32(1), 1–12.
Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool.
Luttrell, R., Davis, J., Welch, C., & Fiegel, C. (2025). The 2024–2025 fluency report: Bridging the AI digital divide. Emerging Insights Lab, S. I. Newhouse School, Syracuse University.
Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO.
Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. UNESCO.
Ministero dell’Istruzione e del Merito. (2025). Linee guida per l’introduzione dell’intelligenza artificiale nelle istituzioni scolastiche (Versione 1.0).
Moura, A., & Carvalho, A. (2024). Teachers’ perceptions of the use of artificial intelligence in the classroom. In O. Titrek et al. (Eds.), International Conference on Lifelong Education and Leadership for All: ICLEL 2023 (pp. 140–150). Atlantis Press.
Nussbaum, M. C. (2011). Creating capabilities: The human development approach. Harvard University Press.
Pasquale, F. (2020). New laws of robotics: Defending human expertise in the age of AI. Harvard University Press.
Ranieri, M., Cuomo, S., & Biagini, G. (2026). The dimensions of artificial intelligence literacy: A pedagogical perspective. In Artificial intelligence in school (Digital Education and Learning). Palgrave Macmillan.
https://doi.org/10.1007/978-3-032-15919-9_1
Romero-Tena, R., Martínez-Navarro, R., & León-Garrido, A. (2025). Training of future teachers in the binomial universal design for learning and technologies for inclusive education. Sustainability, 17(14), Article 6504.
Rondonotti, M., & Emanuel, F. (2026, in press). Il questionario PAIR (Participatory AI for Inclusive Relationships): Un contributo per indagare l’integrazione dell’AI nei servizi socio-educativi. In Atti del Convegno SIRD 2025.
Rosa, H. (2015). Accelerazione e alienazione: Per una teoria critica nella tarda modernità. Einaudi.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Selwyn, N., Ljungqvist, M., & Sonesson, A. (2025). When the prompting stops: Exploring teachers’ work around the educational frailties of generative AI tools. Learning, Media and Technology, 1–14.
Sen, A. (1985). Commodities and capabilities. Oxford University Press.
Singh, A., & Gupta, P. (2025). Perspective of generative AI for UNESCO inclusive education. SSRN. https://ssrn.com/abstract=5181859
Stahl, B. C., Antoniou, J., Bhalla, N., Brooks, L., Jansen, P., Lindqvist, B., & Wright, D. (2023). A systematic review of artificial intelligence impact assessments. Artificial Intelligence Review, 56(11), 12799–12831.
Susskind, R., & Susskind, D. (2015). The future of the professions: How technology will transform the work of human experts. Oxford University Press.
Tulchinsky, I., & Mason, C. E. (2023). The age of prediction: Algorithms, AI, and the shifting shadows of risk. MIT Press.
UNESCO. (2025). AI and the future of education: Disruptions, dilemmas and directions. UNESCO.
van Dijck, J., Poell, T., & de Waal, M. (2018). The platform society: Public values in a connective world. Oxford University Press.
Van Mechelen, I., Boulesteix, A.-L., Dangl, R., Dean, N., Hennig, C., Leisch, F., & Steinley, D. (2023). A white paper on good research practices in benchmarking: The case of cluster analysis. WIREs Data Mining and Knowledge Discovery, Article e1511.
Walker, M., & Unterhalter, E. (2007). Amartya Sen’s capability approach and social justice in education. Springer.
Williamson, B., Macgilchrist, F., & Potter, J. (2023). Re-examining AI, automation and datafication in education. Learning, Media and Technology, 48(1), 1–5.
DOI: https://doi.org/10.32043/gsd.v10i3.1811
Refbacks
- There are currently no refbacks.
Copyright (c) 2026 ITALIAN JOURNAL OF HEALTH EDUCATION, SPORT AND INCLUSIVE DIDACTICS

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Italian Journal of Health Education, Sports and Inclusive Didactics
Publisher: Cares srl – REN Press (since 2026). Previously published by Gaia srl – Edizioni Universitarie Romane (2017–2025).
ISSN: 2532-3296 E-ISSN: 3103-5744
Italian Journal of Health Education, Sports and Inclusive Didactics
