Perceptions of Generative AI for Inclusive Instruction Among Educators
Parole chiave:
artificial intelligence, special education, ethics, professional development, accessibility, technologyAbstract
This study explored the role of generative artificial intelligence (GenAI) in supporting inclusive and accessible learning environments using survey data from over 800 educators. As tools such as ChatGPT are rapidly integrated into classrooms, offering potential support for differentiation, accessibility features, and administrative tasks, questions remain regarding educators’ preparedness and confidence to use these tools effectively. This research examined educators’ perceptions of the benefits and ethical risks of GenAI, particularly as they related to instruction for students with diverse learning needs. Using a mixed-methods survey approach, the study investigated whether this emergent technology had the potential to advance accessible learning for all or instead, contribute to existing systemic inequities affecting vulnerable students. Results showed that educators used GenAI with purpose, and considered how it fit with their instructional responsibilities, beliefs about learning, and commitments to equitable support for students. The educators described weighing potential benefits for personalization and differentiation against concerns about ethical use, accuracy, and students’ independence and critical thinking. Overall, the findings suggest that GenAI use is often moving ahead of formal training and institutional guidance, highlighting the need for clearer ethical expectations and targeted professional learning to support equitable and accessible implementation.
Riferimenti bibliografici
Al-Amin, M., Shazed Ali, M., Salam, A., Khan, A., Ali, A., Ullah, A., & Chowdhury, S. K. (2024). History of generative artificial intelligence (AI) chatbots: Past, present, and future development (arXiv:2402.05122). arXiv. https://arxiv.org/abs/2402.05122
Ayala, M. C. (2024). ChatGPT as a universal design for learning tool supporting college students with disabilities. Educational (Re)naissance, 12(1), 23–41. https://doi.org/10.33499/edren.v12i1.3866
Bartlett, I., Gikas, J., Hon, N. S., Kupatadze, I., & Shchotkina, M. (2025, August 20). The rise of generative AI: Opportunities and challenges (I). OxJournal. https://www.oxjournal.org/the-rise-of-generative-ai/
De Giuseppe, T., Sozio, A., Carbone, M., Delello, J. (2025a). The Intersection Of Artificial Intelligence And Prosocial Behavior. Proceedings of The 1st International Scientific Conference Education and Artificial Intelligence (EDAI 2024) pp. 91 – 99. a cura di Aleksandar Spasić, Darko Stojanović]. - Vranje : Università di Niš, Facoltà Pedagogica, 2025. ISBN 978-86-6301-060-4 https://doi.org/10.46793/EDAI24.091G
De Giuseppe, T. Catalano E, Tornusciolo, S., Carbone, M.(2025b The impact of new inclusive models in times of educational inequalities: flipped inclusion and the challenges of pedagogy. Journal of inclusive methodology and technology in teaching 5 (3)1-10. Edizioni Universitarie Romane. ISSN 2785-5104 https://doi.org/10.32043/jimtlt.v5i3
Delello, J. A., Watters, J. B., & Garcia-Lopez, A. (2024). Artificial intelligence in education: Transforming learning and teaching. In J. A. Delello & R. R. McWhorter (Eds.), Disruptive technologies in education and workforce development (pp. 1-26). IGI Global. https://doi.org/10.4018/979-8-3693-3003-6
Delello, J. A., Sung, W., Mokhtari, K., Hebert, J., Bronson, A., & De Giuseppe, T. (2025). AI in the classroom: Insights from educators on usage, challenges, and mental health. Education Sciences, Special Issue: How Artificial Intelligence Can Enhance Education: Current Practices and Challenges, 15(2), 1-27. https://doi.org/10.3390/educsci15020113
Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006
Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernandez-Leo, D., & Rienties, B. (2024). The promise and challenges of generative AI in education. Behaviour & Information Technology, 44(11), 2518–2544. https://doi.org/10.1080/0144929X.2024.2394886
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023). Adaptive learning using artificial intelligence in e-learning: A literature review. Education Sciences, 13(12), 1216. https://doi.org/10.3390/educsci13121216
Goldman, S. R., Taylor, J., Carreon, A., & Smith, S. J. (2024). Using AI to support special education teacher workload. Journal of Special Education Technology, 39(3), 434-447. https://doi.org/10.1177/01626434241257240
He, R., Cao, J., & Tan, T. (2025). Generative artificial intelligence: A historical perspective. National Science Review, 12(5). https://doi.org/10.1093/nsr/nwaf050
Hester, O. R., Bridges, S. A., & Rollins, L. H. (2020). ‘Overworked and underappreciated’: special education teachers describe stress and attrition. Teacher Development, 24(3), 348–365. https://doi.org/10.1080/13664530.2020.1767189
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
International Institute for Educational Planning–UNESCO. (2019). On the road to inclusion: Highlights from the UNICEF and IIEP technical round tables on disability-inclusive education sector planning. https://unesdoc.unesco.org/ark:/48223/pf0000372193
ISTAT. (2025). Alunni con disabilità: Anno scolastico 2023–2024 [Students with disabilities: School year 2023–2024]. Italian National Institute of Statistics. https://www.istat.it
Jain, A. (2025, June 5). Designing for ethical and inclusive AI through a human-centered design lens. Global Business & Economics Journal. https://gbej.org/articles/designing-for-ethical-and-inclusive-ai-through-a-human-centered-design-lens/
Kalantzis, M., & Cope, B. (2025). Literacy in the time of artificial intelligence. Reading Research Quarterly, 60(1), Article e591. https://doi.org/10.1002/rrq.591
Khlaif, Z. N., Alshakhshir, R., Hamamra, B., & Joma, A. (2025). Reimagining inclusive education: The assistive power of generative AI in promoting accessibility and equity. British Journal of Visual Impairment. https://doi.org/10.1177/02646196251382469
Lopez-Gazpio, I. (2025). Integrating large language models into accessible and inclusive education: Access democratization and individualized learning enhancement supported by generative artificial intelligence. Information, 16(6), 473. https://doi.org/10.3390/info16060473
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27(4), 12-14. https://doi.org/10.1609/aimag.v27i4.1904
Melo-López, V.-A., Basantes-Andrade, A., Gudiño-Mejía, C.-B., & Hernández-Martínez, E. (2025). The impact of artificial intelligence on inclusive education: A systematic review. Education Sciences, 15(5), 539. https://doi.org/10.3390/educsci15050539
OpenAI. (2025). How people are using ChatGPT. https://openai.com/index/how-people-are-using-chatgpt/
Open Innovation Team & Department for Education. (2024). Generative AI in education: Educator and expert views. https://files.eric.ed.gov/fulltext/ED649949.pdf
Saborío-Taylor, S., & Rojas-Ramírez, F. (2024). Universal design for learning and artificial intelligence in the digital era: Fostering inclusion and autonomous learning. International Journal of Professional Development, Learners and Learning, 6(2), ep2408. https://doi.org/10.30935/ijpdll/14694
Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022). Towards a standard for identifying and managing bias in artificial intelligence (NIST Special Publication 1270). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.1270
Storey, V. C., Yue, W. T., Zhao, J. L., & Lukyanenko, R. (2025). Generative artificial intelligence: Evolving technology, growing societal impact, and opportunities for information systems research. Information Systems Frontiers. Advance online publication. https://doi.org/10.1007/s10796-025-10581-7
Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433
UNESCO. (2017). A guide for ensuring inclusion and equity in education. UNESCO. http://unesdoc.unesco.org/images/0024/002482/248254e.pdf
UNESCO. (2019). Beijing consensus on artificial intelligence and education. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000368303
UNESCO. (2020). Global education monitoring report 2020: Inclusion and education – All means all. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000373718
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
UNICEF. (2021). Seen, counted, included: Using data to shed light on the well-being of children with disabilities. UNICEF. https://www.unicef.org/reports/seen-counted-included-children-with-disabilities-2021
U.S. Department of Education. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://www.ed.gov
Valle Escolano, R. (2023). Artificial intelligence and rights of people with disabilities: The power of algorithms. Revista Española de Discapacidad, 11(1), 29–49. https://doi.org/10.5569/2340-5104.11.01.03
Wang, M., Tlili, A., Khribi, M. K., Lo, C. K., & Huang, R. (2025). Generative artificial intelligence in special education: A systematic review through the lens of the mediated-action model. Information Development. Advance online publication. https://doi.org/10.1177/02666669251335655
Waterfield, D. A., Coleman, O. F., Welker, N. P., Kennedy, M. J., McDonald, S. D., & Cook, B. G. (2025). IEPs in the age of AI: Examining IEP goals written with and without ChatGPT. Journal of Special Education Technology. https://doi.org/10.1177/01626434251324592
Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45. https://doi.org/10.1145/365153.365168
Zhao, X., Chen, X., & Cox, A. (2025). Exploring the affordances of generative AI in academic writing for students with disabilities: A bottom-up approach to inform GenAI policies. Policy Futures in Education, 0(0). https://doi.org/10.1177/14782103251395436
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