Ensuring equal access to quality education is a worldwide issue, particularly for learners vulnerable to exclusion because of their ethnicity, language, religion, nationality, or disabilities. This research examines the capabilities of generative artificial intelligence (Gen AI) to close disparities in inclusive education, particularly for undergraduate students with visual disabilities in Palestine. Employing a qualitative case study method, semi-structured interviews were carried out with 21 participants to obtain detailed insights. This study addresses three key inquiries: the role of Gen AI as a supportive instrument, essential technological features that foster inclusivity, and the ways in which Gen AI enhances access and participation. Thematic analysis revealed themes of adaptability, accessibility, supportive teaching, equity, and educational quality, as well as technological features including interactivity, user-friendliness, cost-effectiveness, scalability, integration, and multimodal learning. Results suggest that Gen AI improves tailored learning, streamlines content, assists teachers, and closes the digital gap. Ethical considerations are crucial for reducing biases and promoting fairness. This research contributes to models concerning adaptive learning technologies and provides practical insights for promoting inclusive educational settings.
Abu ShokhedimS.KhlaifZ. N.HattabM. K.ItmaziJ. (2025). Digital empowerment of undergraduate students with vision disability. British Journal of Visual Impairment. Advance online publication. https://doi.org/10.1177/02646196251330161
2.
AdmaneR.SawaleP. S.JayasreeR.KurupS. J.ThomasS. A. (2024). Artificial intelligence in education: Tailoring curriculum to individual student needs through AI-based systems. Library of Progress-library Science, Information Technology & Computer, 44(3), Article 4421.
3.
AlmufarehM. F.KausarS.HumayunM.TehsinS. (2024). A conceptual model for inclusive technology: Advancing disability inclusion through artificial intelligence. Journal of Disability Research, 3(1), Article 20230060.
4.
AlshakhshirR. (2023). Perceptions of pre-service teachers in palestine towards learners with disabilities: A critical constructivist analysis [Degree Of Doctor of Philosophy]. University of Dundee, United Kingdom.
5.
Al-TaaiS. H. H. (2022). Information technology: A study of its concept and importance in educational institutions. Webology, 19(1), 3369–3391.
6.
Ariyo OkaiyetoS.BaiJ.XiaoH. (2023). Generative AI in education: To embrace it or not. International Journal of Agricultural and Biological Engineering, 16(3), 285–286.
7.
AyyoubA. M.KhlaifZ. N.ShamaliM.Abu EidehB.AssaliA.HattabM. K.. . . BsharatT. R. K. (2025). Advancing higher education with GenAI: Factors influencing educator AI literacy. Frontiers in Education, 10, 1530721.
8.
BakerT.SmithL.AnissaN. (2019). Educ-AI-tion Rebooted? Exploring the Future of Artificial Intelligence in Schools and Colleges. Nesta.
9.
BinY.MandalD. (2019). English teaching practice based on artificial intelligence technology. Journal of Intelligent & Fuzzy Systems, 37(3), 3381–3391.
10.
BraunV.ClarkeV. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
11.
BulathwelaS.Pérez-OrtizM.HollowayC.CukurovaM.Shawe-TaylorJ. (2024). Artificial intelligence alone will not democratise education: On educational inequality techno-solutionism and inclusive tools. Sustainability, 16(2), Article 781. https://doi.org/10.3390/su16020781
12.
ChiuT. K. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(Suppl. 1), S14–S30.
13.
ChopraA.PatelH.RajputD. S.BansalN. (2024). Empowering inclusive education: Leveraging AI-ML and innovative tech stacks to support students with learning disabilities in higher education. In KaluriR.MahmudM.GadekalluT. R.RajputD. S.LakshmannaK. (Eds.), Applied assistive technologies and informatics for students with disabilities (pp. 255–275). Springer Nature.
14.
ClarkP.EtzioniO. (2016). My computer is an honor student: But how intelligent is it? Standardized tests as a measure of AI. AI Magazine, 37(1), 5–12.
15.
CreswellJ. W. (2009). Mapping the field of mixed methods research. Journal of Mixed Methods Research, 3(2), 95–108.
16.
FitriaT. N. (2021b). Grammarly as AI-powered English writing assistant: Students’ alternative for writing English. Metathesis: Journal of English Language, Literature, and Teaching, 5(1), 65–78.
17.
FitriaT. N. (2021c). The use technology based on artificial intelligence in English teaching and learning. ELT Echo: The Journal of English Language Teaching in Foreign Language Context, 6(2), 213–223.
18.
GargG.PrabhaC.SharmaN.KumariS. (2025, April). Health care for people with disabilities in rural areas. In 2025 Seventh International Conference on Computational Intelligence and Communication Technologies (CCICT) (pp. 526–531). IEEE.
19.
GargP. K.SharmaL. (2021). Artificial intelligence: Challenges and future applications. In Artificial Intelligence (pp. 229–245). Chapman and Hall/CRC.
20.
GasimovaS. (2023). Вісник післядипломної освіти: збірник наукових праць: Серія «Педагогічні науки» [Use of information and communication technologies as a means of efficiency of Azerbaijani history lessons in secondary schools]. ПОЧАТОК, 26(55), 76–88.
21.
GilT. O.Jr (2025). Inclusive education in higher education institutions: A conceptual framework for implementation in support of sustainable development goals (SDGs). Indonesian Journal of Community and Special Needs Education, 5(2), 107–120.
22.
KadaruddinK. (2023). Empowering education through generative AI: Innovative instructional strategies for tomorrow’s learners. International Journal of Business, Law, and Education, 4(2), 618–625.
23.
KhlaifZ. N.AlkoukW. A.SalamaN.Abu EidehB. (2025). Redesigning assessments for AI-enhanced learning: A framework for educators in the generative AI era. Education Sciences, 15(2), 174.
24.
KhlaifZ. N.AyyoubA.HamamraB.BensalemE.MitwallyM. A.AyyoubA.. . . ShadidF. (2024). University teachers’ views on the adoption and integration of generative AI tools for student assessment in higher education. Education Sciences, 14(10), 1090.
25.
KhlaifZ. N.MousaA.HattabM. K.ItmaziJ.HassanA. A.SanmugamM.AyyoubA. (2023). The potential and concerns of using AI in scientific research: ChatGPT performance evaluation. JMIR Medical Education, 9, e47049.
26.
KlammaR.de LangeP.NeumannA. T.HensenB.KravcikM.WangX.KuzilekJ. (2020, June). Scaling mentoring support with distributed artificial intelligence. In International conference on intelligent tutoring systems (pp. 38–44). Springer.
27.
KoY.IssenbergS. B.RohY. S. (2022). Effects of peer learning on nursing students’ learning outcomes in electrocardiogram education. Nurse Education Today, 108, Article 105182.
28.
LincolnY. S.GubaE. G. (1985). Naturalistic Inquiry. Sage.
29.
LuS. C. (2025). How engagement in drama may foster self-awareness and supportive interactions in class. Research in Drama Education: The Journal of Applied Theatre and Performance, 1–20. https://doi.org/10.1080/13569783.2024.2441153
30.
MaoZ.LiM. (2023). Artificial intelligence and cognitive diagnosis based teaching resource recommendation algorithm. PeerJ Computer Science, 9, e1594.
31.
NixonN.LinY.SnowL. (2024). Catalyzing equity in STEM teams: Harnessing generative AI for inclusion and diversity. Policy Insights from the Behavioral and Brain Sciences, 11(1), 85–92.
32.
PattonM. Q. (2002). Two decades of developments in qualitative inquiry: A personal, experiential perspective. Qualitative Social Work, 1(3), 261–283.
33.
ŞenN.AkbayT. (2023). Artificial intelligence and innovative applications in special education. Instructional Technology and Lifelong Learning, 4(2), 176–199.
34.
SharmaS.TomarV.YadavN.AggarwalM. (2023). Impact of AI-based special education on educators and students. In KumarA.NayyarA.SachanR. K.JainR. (Eds.), AI-assisted special education for students with exceptional needs (pp. 47–66). IGI Global.
35.
ShoaibM.SayedN.SinghJ.ShafiJ.KhanS.AliF. (2024). AI student success predictor: Enhancing personalized learning in campus management systems. Computers in Human Behavior, 158, Article 108301.
36.
TangY. M.ChenP. C.LawK. M.WuC. H.LauY. Y.GuanJ.. . . HoG. T. (2021). Comparative analysis of student’s live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. Computers & Education, 168, 104211.
WangC.ZouJ.XieZ. (2024, August). AI-powered educational data analysis for early identification of learning difficulties. In The 31st international scientific and practical conference ‘methodological aspects of education: Achievements and prospects’ (August 06–09, 2024) Rotterdam, Netherlands (p. 223). International Science Group.
41.
WeissbrodtD.FarleyR. (1994). The UNESCO human rights procedure: An evaluation. Human Rights Quarterly,16, 391.
42.
WertheimerA. (1997). Inclusive education: A framework for change. National and International Perspectives. ERIC.
43.
YadavA.KantR.KumarV. (2025). Examining the sustainable development goals (SDGs) attained through adoption of industry 4.0 practices in manufacturing: A hybrid spherical fuzzy BBWM-MAIRCA framework approach. Business Strategy & Development, 8(3), e70190.
44.
YakubovA.NazarovY.RodionovA. A. (2024, June). Advancing e-learning and m-learning environments incorporating AI and gamification to boost learner motivation. In Proceedings of the 2024 4th international conference on technology enhanced learning in higher education (TELE) (pp. 29–31). Institute of Electrical and Electronics Engineers.
45.
YambalS.WaykarY. A. (2025). Future of education using adaptive AI, intelligent systems, and ethical challenges. In GregoryS. (Ed.), Effective instructional design informed by AI (pp. 171–202). IGI Global.
46.
ZawawiA.KmailZ.AlshamaliM. (2024). The practice of digital citizenship values among higher primary school students in Jenin governorate of Palestine. An-Najah University Journal for Research-B (Humanities), 39(7), 505–516.
47.
ZhangH.LeeI.AliS.DiPaolaD.ChengY.BreazealC. (2023). Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: An exploratory study. International Journal of Artificial Intelligence in Education, 33(2), 290–324.
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
