AI in education: Evidence from cluster-based profiling of social educators and teachers in Italy
Main Article Content
Abstract
The paper examines how professionals working in Italian educational contexts - social educators and teachers - perceive and use Artificial Intelligence (AI) in their professional practice. Conducted within the TEACH-AI project, the study is based on questionnaire data collected from 400 social educators and more than 4,000 curricular and support teachers. The aim is to identify and compare empirically grounded profiles of engagement with AI by analyzing patterns of affordance-in-practice, professional capability, and sentiment. Cluster analyses identified four recurring profiles - Receptive, Adaptive, Oppositional, and Indifferent - showing structurally similar configurations across professional roles. Findings reveal a gap between the predominantly operational use of AI tools and their perceived potential to support reflective, inclusive, and relational educational practices. The paper discusses how cluster-based profiling can inform the design of adaptive and differentiated professional development pathways, moving beyond an understanding of AI integration based solely on levels of adoption.
Article Details
Section

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons CC BY 4.0 Attribution 4.0 International License.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access)
References
Adamoli, M., Marangi, M., Rondonotti, M., & Raviolo, P. (2025). AI literacy: An explorative analysis of Italian university students’ perceptions of AI skills. Italian Journal of Health Education, Sport and Inclusive Didactics, 9(1). https://doi.org/10.32043/gsd.v9i1.1321
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. https://doi.org/10.6093/2284-0184/13131
Adamoli, M., Emanuel, F., Marangi, M., Rondonotti, M., & Raviolo, P. (2026b). AI Literacy and Integration in Socio-educational Contexts: The TEACH-AI Project. In M. Adamoli, D. Amato, D., A. Ciasullo, G. Fulantelli, M. Rondonotti, F. Santoianni, D. Schicchi, & D. Taibi, D. (Eds), 7th International Conference on Higher Education Learning Methodologies and Technologies Online. Springer.
Al-Zahrani, A. (2024). The impact of generative AI tools on researchers and research: Implications for academia in higher education. Innovations in Education and Teaching International, 61(5), 1023–1043. https://doi.org/10.1080/14703297.2023.2271445
Anand, P., Jones, S., Donoghue, M., & Teitler, J. (2021). Non-monetary poverty and deprivation: A capability approach. Journal of European Social Policy, 31(1), 78–91. https://doi.org/10.1177/0958928720938334
Biagini, G. (2025). Towards an AI-literate future: A systematic literature review exploring education, ethics, and applications. International Journal of Artificial Intelligence in Education, 35, 2616–2666. https://doi.org/10.1007/s40593-025-00466-w
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. https://doi.org/10.17705/1thci.00138
Conole, G., & Dyke, M. (2004). What are the affordances of information and communication technologies? ALT-J: Research in Learning Technology, 12(2), 113–124. https://doi.org/10.1080/0968776042000216183
Cosgrove, J., & Cachia, R. (2025). DigComp 3.0: European digital competence framework (5th ed.). Publications Office of the European Union. https://doi.org/10.2760/0001149
Costa, E. (2018). Affordances-in-practice: An ethnographic critique of social media logic and context collapse. New Media & Society, 20(10), 3641–3656. https://doi.org/10.1177/1461444818756290
Council of Europe Education Department. (2025). AI literacy for human rights, democracy and social agency: A three-dimensional AI literacy framework. Council of Europe. https://rm.coe.int/ai-literacy-for-human-rights-democracy-and-social-agency/1680b4f9d4
Damioli, G., Van Roy, V., & Vertesy, D. (2021). The impact of artificial intelligence on labor productivity. Eurasian Business Review, 11, 1–25. https://doi.org/10.1007/s40821-020-00172-8
Doss, C. J., Bozick, R., Schwartz, H. L., Chu, L., Rainey, L. R., Woo, A., Reich, J., & Dukes, J. (2025). AI use in schools is quickly increasing but guidance lags behind: Findings from the RAND Survey Panels (Research Report RR-A4180-1). RAND Corporation. https://doi.org/10.7249/RRA4180-1
European Commission. (2020). Digital Education Action Plan 2021–2027: Resetting education and training for the digital age. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0624
European Commission, Directorate-General for Education, Youth, Sport and Culture. (2022). Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Publications Office of the European Union. https://doi.org/10.2766/153756
European Parliament & Council of the European Union. (2016). Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Official Journal of the European Union, L 119, 1–88. http://data.europa.eu/eli/reg/2016/679/oj
European Parliament & Council of the European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (Artificial Intelligence Act). Official Journal of the European Union, L, 2024/1689. http://data.europa.eu/eli/reg/2024/1689/oj
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. https://doi.org/10.1007/s41134-024-00326-y
Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., Järvelä, S., Mavrikis, M., & Rienties, B. (2025). The promise and challenges of generative AI in education. Behaviour & Information Technology, 44(11), 2518–2544. https://doi.org/10.1080/0144929X.2024.2394886
Hammond, M. (2010). What is an affordance and can it help us understand the use of ICT in education? Education and Information Technologies, 15(3), 205–217. https://doi.org/10.1007/s10639-009-9106-z
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. https://doi.org/10.2307/2346830
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. https://doi.org/10.1093/bjsw/bcab168
Hodgson, D., Watts, L., & Gair, S. (2023). Artificial intelligence and implications for the Australian social work journal. Australian Social Work, 76(4), 425–427. https://doi.org/10.1080/0312407X.2023.2247833
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. https://doi.org/10.1002/(SICI)1097-0266(199606)17:6%3C441::AID-SMJ819%3E3.0.CO;2-G
Kitsara, I. (2022). Artificial intelligence and the digital divide: From an innovation perspective. In A. Bounfour (Ed.), Platforms and artificial intelligence (Progress in IS). Springer. https://doi.org/10.1007/978-3-030-90192-9_12
Lamacchia, M., Carruba, M., Dicataldo, M. C., & Dipace, A. (2025). Artificial intelligence for inclusion: Teachers’ perceptions. Italian Journal of Special Education for Inclusion, 13(1), 82–95. https://doi.org/10.7346/sipes-01-2025-6
Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers.
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). ACM. https://doi.org/10.1145/3313831.3376727
Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: Guidance for policy-makers. UNESCO. https://doi.org/10.54675/PCSP7350
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084
Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. UNESCO. https://doi.org/10.54675/JKJB9835
MIM - Ministero dell’Istruzione e del Merito. (2025). Linee guida per l’introduzione dell’intelligenza artificiale nelle istituzioni scolastiche (Version 1.0). https://www.mim.gov.it/documents/20182/0/MIM_Linee+guida+IA+nella+Scuola_09_08_2025-signed.pdf/b70fdc45-4b75-1f7e-73bf-eab12989b928
Mishra, P., Warr, M., & Islam, R. (2023). TPACK in the age of ChatGPT and generative AI. Journal of Digital Learning in Teacher Education, 39, 1–17. https://doi.org/10.1080/21532974.2023.2247480
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
Nussbaum, M. C. (2011). Creating capabilities: The human development approach. Harvard University Press.
OECD - Organisation for Economic Co-operation and Development. (2024). Recommendation of the Council on Artificial Intelligence. OECD Legal Instruments. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
OECD - Organisation for Economic Co-operation and Development. (2025). Empowering learners for the age of AI: An AI literacy framework for primary and secondary education. https://ailiteracyframework.org
Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined Technology Readiness Index: TRI 2.0. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730
Pasquale, F. (2020). New laws of robotics: Defending human expertise in the age of AI. Harvard University Press.
Reamer, F. G. (2023). Artificial intelligence in social work: Emerging ethical issues. International Journal of Social Work Values and Ethics, 20(2), 52–71. https://doi.org/10.55521/10-020-205
Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office of the European Union. https://doi.org/10.2760/159770
Robeyns, I. (2005). The Capability Approach: a theoretical survey. Journal of Human Development, 6(1), 93–117. https://doi.org/10.1080/146498805200034266
Rondonotti, M., & Emanuel, F. (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.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Sen, A. (1985). Commodities and capabilities. Oxford University Press.
Sen, A. (1999). Development as Freedom. Oxford University Press
Tan, S., Li, L., & Sha, L. (2025). Teacher professional development in the age of AI: A systematic review. Teaching and Teacher Education, 152, 104826. https://doi.org/10.1016/j.tate.2025.104826
UNESCO. (2019). Beijing consensus on artificial intelligence and education. https://unesdoc.unesco.org/ark:/48223/pf0000368303
Van Mechelen, I., Boulesteix, A.-L., Dangl, R., Dean, N., Hennig, C., Leisch, F., Steinley, D., & Warrens, M. J. (2023). A white paper on good research practices in benchmarking: The case of cluster analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(6), e1511. https://doi.org/10.1002/widm.1511
Williamson, B., Gulson, K. N., Perrotta, C., & Witzenberger, K. (2023). Amazon and the new global connective architectures of education governance. Journal of Education Policy, 38(3), 361–381. https://doi.org/10.1080/02680939.2022.2030573
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on AI applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), Article 39. https://doi.org/10.1186/s41239-019-0171-0