Artificial Intelligence and Inclusive History Teaching: A Mixed-Methods Study in a Middle School in Naples
Parole chiave:
Artificial Intelligence, Engagement, MotivationAbstract
Integrating generative artificial intelligence into classroom practice is emerging as a strategic lever for inclusive innovation, particularly in history education. This study examines the effectiveness of using the Suno.com generative audio platform, in combination with theatrical and narrative activities, within history instruction in a lower secondary (middle) school in Naples, Italy. Adopting a mixed-methods design, we involved 150 students—10 diagnosed with Autism Spectrum Disorder (ASD)—evenly assigned to experimental and control groups. Standardized instruments, including the School Engagement Questionnaire (SEQ) and the Psychological Sense of School Membership (PSSM), were administered alongside semi-structured interviews analyzed in NVivo. Results indicate a significant improvement in school engagement, motivation, and sense of belonging among students in the experimental group, with particularly positive effects for those with Special Educational Needs (SEN), including ASD. Findings suggest that, when embedded in reflective and cooperative pedagogical environments, AI can meaningfully support the development of more equitable, accessible, and personalized learning pathways, reimagining history teaching as an experiential and participatory practice.
Riferimenti bibliografici
American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.). American Psychiatric Publishing.
Beetham, H., & Sharpe, R. (Eds.). (2019). Rethinking pedagogy for a digital age: Principles and practices of design (3rd ed.). Routledge.
CAST. (2018). Universal Design for Learning Guidelines version 2.2. https://udlguidelines.cast.org
Cornoldi, C., & De Beni, R. (2020). Strategie di apprendimento e metacognizione. Il Mulino.
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage.
Flick, U. (2018). An introduction to qualitative research (6th ed.). Sage.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109.
Fredricks, J. A., Wang, M. T., & Reeve, J. (2020). Engagement and motivation in school. In R. M. Ryan (Ed.), The Oxford handbook of human motivation (2nd ed., pp. 491–508). Oxford University Press.
Guldberg, K., Parsons, S., Porayska-Pomsta, K., & Lee, R. (2015). Digital learning for children with autism and complex learning needs: A systematic framework for research and practice. Learning, Media and Technology, 40(3), 300–318.
Goodenow, C. (1993). The psychological sense of school membership among adolescents: Scale development and educational correlates. Psychology in the Schools, 30(1), 79–90.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Johnson, D. W., & Johnson, R. T. (2019). Cooperative learning: The foundation for active learning. In Active learning: Strategies to enhance learning in higher education (pp. 13–30). Springer.
Laurillard, D. (2021). Teaching as a design science: Building pedagogical patterns for learning and technology. Routledge.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Martínez-Muñoz, G., Rodríguez-García, A. M., & Fernández-Batanero, J. M. (2022). Teachers’ digital competence and inclusive education: A systematic review. Education and Information Technologies, 27(1), 287–308.
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13.
OECD. (2023). Ethical guidelines for educational research and innovation. OECD Publishing.
Pagliaro, S., Di Santo, D., & Coppola, G. (2022). Appartenenza scolastica e resilienza nei contesti a rischio. Psicologia della Scuola, 44(2), 105–124.
Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education
Reeve, J., Ryan, R. M., Deci, E. L., & Jang, H. (2020). Understanding and promoting autonomous motivation in students: A self-determination theory perspective. In The Oxford handbook of school psychology (pp. 57–78). Oxford University Press.
Schuelka, M. J., Johnstone, C. J., Thomas, G., & Artiles, A. J. (2019). The SAGE handbook of inclusion and education. Sage.
Sousa, D. A. (2022). How the brain learns (6th ed.). Corwin Press.
Wineburg, S. (2018). Why learn history (when it’s already on your phone). University of Chicago Press.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2023). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 20(1), 1–38.
##submission.downloads##
Pubblicato
Come citare
Fascicolo
Sezione
Licenza
Copyright (c) 2025 Gianluca Gravino, Federica Badii Esposito, Lucia Ariemma
Questo lavoro è fornito con la licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 4.0 Internazionale.
