The Role of Artificial Intelligence in the Establishment of Inclusive Learning Environments: A Conceptual Synthesis

Authors: Dr. Gabriel Julien
The Role of Artificial Intelligence in the Establishment of Inclusive Learning Environments: A Conceptual Synthesis
DOI
10.25125/jcrelc-feb-2026-1
DIN
JCRELC-FEB-2026-1
Abstract

The rapid integration of artificial intelligence (AI) in educational settings has generated significant interest in its potential to support inclusive learning environments. This conceptual analysis examines how AI technologies can be aligned with Universal Design for Learning (UDL) principles to promote accessibility, engagement, and instructional flexibility for diverse learners. The study synthesizes contemporary theoretical and policy-oriented literature to analyze AI's role in reducing barriers to learning. AI tools—including adaptive learning platforms, assistive technologies, intelligent tutoring systems, and learning analytics—are examined as mechanisms for operationalizing UDL in inclusive classrooms, particularly in their capacity to personalize instruction, provide accessible content formats, and support varied modes of learner expression. The analysis considers how AI systems can complement evidence-based practices in special education, including differentiated instruction and individualized education planning. Alongside instructional benefits, the research critically examines ethical and implementation challenges associated with AI in UDL-aligned settings, including algorithmic bias, data privacy, transparency, and unequal access to technology as potential threats to educational equity. The paper argues that without intentional alignment with UDL frameworks and adequate policy safeguards, AI may inadvertently reinforce existing disparities. It concludes by emphasizing the need for educator professional development, ethical design standards, and inclusive governance structures to ensure AI functions as a supportive tool for inclusion. Continued conceptual and policy-focused research is recommended to guide responsible AI integration in special education contexts.

Keywords
Artificial intelligence Inclusive education Universal Design for Learning Educational technology Educational equity Inclusive pedagogy.
Download Options
Download Full PDF
Share Article
Facebook Twitter LinkedIn
Introduction

Inclusive education represents a central priority in contemporary educational systems, reflecting commitments to equity and accessibility through meaningful participation for all learners, particularly students with disabilities and historically marginalized groups (Ainscow, 2020; UNESCO, 2020). Inclusive environments accommodate diverse learners by reducing barriers to access, maximizing engagement and achievement, and fostering belonging within general education settings. Despite policy advances, educational institutions continue to face challenges in meeting diverse learner needs, with conventional teaching approaches and resource limitations impeding successful inclusive practices (Florian, 2019; Losberg & Zwozdiak-Myers, 2024).

Artificial intelligence (AI) has emerged as a potentially transformative force in education, offering new possibilities for addressing learner diversity through adaptive, responsive, and data-informed instructional supports (Holmes et al., 2019; Zawacki-Richter et al., 2019). AI encompasses computational systems capable of tasks requiring human intelligence, such as pattern recognition, decision-making, and personalized feedback. The field of AI in education (AIEd) includes applications such as intelligent tutoring systems, assistive technologies, learning analytics, and AI-powered learning platforms increasingly positioned as tools supporting inclusive practices through personalized instruction and enhanced accessibility.

The conceptual alignment between AI and inclusive education is particularly evident through the framework of Universal Design for Learning (UDL), which advocates proactive instructional design providing multiple means of engagement, representation, and action/expression to address learner variability from the outset (CAST, 2018). AI technologies potentially operationalize UDL principles by dynamically adjusting content presentation, pacing, and interaction modes, thereby supporting students with disabilities, multilingual learners, and those with diverse learning profiles (Almeqdad et al., 2023).

However, AI integration in inclusive contexts raises critical ethical and practical concerns. Algorithmic bias, data privacy, transparency, and unequal technology access may undermine inclusive goals if not addressed (Williamson & Eynon, 2020). Without intentional alignment with inclusive pedagogical frameworks and ethical safeguards, AI risks reinforcing rather than alleviating educational inequities.

Given these opportunities and challenges, this conceptual analysis examines how AI technologies can support inclusive education through UDL alignment and investigates the ethical and structural conditions necessary for responsible implementation. The study addresses the research question: How can AI be conceptually aligned with UDL principles to support inclusive learning environments, and what key ethical and implementation contingencies shape this alignment?

Conclusion

This conceptual analysis examined AI's role in establishing inclusive learning environments, emphasizing its potential to address learner variability, promote accessibility, and advance educational equity. Guided by Universal Design for Learning, the analysis highlighted how AI tools can support multiple engagement, representation, and action/expression means when aligned with inclusive pedagogical frameworks.

Findings suggest AI holds significant promise for enhancing inclusive education through personalized learning pathways, adaptive support, and tailored feedback for diverse learner needs. When aligned with inclusive frameworks, AI can reduce participation barriers for students with disabilities, multilingual learners, and marginalized populations.

However, AI is not inherently inclusive. Its impact hinges on deliberate design, ethical deployment, and educator capacity for effective utilization. This study emphasizes situating AI within broader social, ethical, and pedagogical contexts, with algorithmic bias, data privacy, and unequal technology access presenting substantial challenges potentially exacerbating existing inequities if unaddressed. Inclusive learning environments cannot be achieved through technological innovation alone but require sustained attention to policy, professional development, and equity-oriented governance.

AI should be understood as a supportive tool augmenting rather than replacing inclusive teaching practices, with value in augmenting human decision-making, expanding instructional flexibility, and fostering learner agency within inclusive classrooms. This conceptual analysis contributes to AI in education discourse by clarifying AI's role through an inclusive lens and providing foundation for future empirical research. Continued interdisciplinary inquiry is essential to ensure AI advances inclusive education ethically, equitably, and responsively to diverse learner realities worldwide.

References
  1. Adigun, T. A., & Igboechesi, G. P. (2024). Exploring the role of generative artificial intelligence in enhancing information retrieval and knowledge discovery in academic libraries. International Journal of Library and Information Science Studies, 10(2), 1–14. https://dx.doi.org/10.37745/ijliss.15/vol10n2114
  2. Ainscow, M. (2020). Promoting inclusion and equity in education: Lessons from international experiences. Nordic Journal of Studies in Educational Policy, 6(1), 7–16. https://dx.doi.org/10.1080/20020317.2020.1729587
  3. Ainscow, M., & Messiou, K. (2018). Engaging with the views of students to promote inclusion in education. Journal of Educational Change, 19(1), 1–17. https://dx.doi.org/10.1007/s10833-017-9312-1
  4. Almeqdad, Q. I., Alodat, A. M., Alquraan, M. F., Mohaidat, M. A., & Al-Makhzoomy, A. K. (2023). The effectiveness of Universal Design for Learning: A systematic review of the literature and meta-analysis. Cogent Education, 10(1), 2218191. https://dx.doi.org/10.1080/2331186X.2023.2218191
  5. Almumen, H. A. (2020). Universal Design for Learning (UDL) across cultures: The application of UDL in Kuwaiti inclusive classrooms. SAGE Open, 10(4). https://dx.doi.org/10.1177/2158244020969674
  6. Alquraini, T. A., & Rao, S. M. (2020). Assessing teachers’ knowledge, readiness, and needs to implement Universal Design for Learning in classrooms in Saudi Arabia. International Journal of Inclusive Education, 24(1), 103–114. https://dx.doi.org/10.1080/13603116.2018.1452298
  7. Al-Zahrani, A. M. (2025). Evaluating the impact of inclusive and accessible learning technologies on student engagement and academic performance in higher education. International Journal of Inclusive Education. Advance online publication. https://dx.doi.org/10.1080/13603116.2025.2593563
  8. Armstrong, P. W., & Ainscow, M. (2018). School-to-school support within a competitive education system: Views from the inside. School Effectiveness and School Improvement, 29(4), 614–633. https://dx.doi.org/10.1080/09243453.2018.1499534
  9. Barahona, M., David, V., Gallegos, F., Reyes-Rojas, J., Ibaceta-Quijanes, X., & Darwin, S. (2023). Analysing preservice teachers' enactment of the UDL framework to support diverse students in a remote teaching context. System, 114, 103027. https://dx.doi.org/10.1016/j.system.2023.103027
  10. Basham, J. D., Gardner, J. E., & Smith, S. J. (2020). Measuring the implementation of UDL in classrooms and schools: Initial field test results. Remedial and Special Education, 41(4), 231–243. https://dx.doi.org/10.1177/0741932520908015
  11. Belachew, M., Adamu, A., Ashete, A., & Mengistie, S. (2025). The impact of digital empowerment training on the awareness of assistive technologies and digital competence skills among students with visual impairment. Disability and Rehabilitation: Assistive Technology, 20(8), 2872–2884. https://dx.doi.org/10.1080/17483107.2025.2508395
  12. Boateng, F. (2025). The transformative potential of generative AI in academic library access services: Opportunities and challenges. Information Services and Use, 45(1–2), 140–147. https://dx.doi.org/10.1177/18758789251332800
  13. Bray, A., Devitt, A., Banks, J., Sanchez Fuentes, S., Sandoval, M., Riviou, K., Byrne, D., Flood, M., Reale, J., & Terrenzio, S. (2023). What next for Universal Design for Learning? A systematic literature review of technology in UDL implementations at second level. British Journal of Educational Technology, 55(1), 113–138. https://dx.doi.org/10.1111/bjet.13328
  14. Bridges, L., Lopezosa, C., Centelles Velilla, M., & Ferran-Ferrer, N. (2025). Moving towards critical AI literacy in LIS education: A scoping review and syllabi analysis. The Electronic Library, 43(5), 798–817. https://dx.doi.org/10.1108/EL-06-2025-0224
  15. Bright, D. (2022). An integrative review of the potential of wireless assistive technologies and Internet of Things (IoT) to improve accessibility to education for students with disabilities. Assistive Technology, 34(6), 653–660. https://dx.doi.org/10.1080/10400435.2021.1956639
  16. Bubb, S., Crossley-Holland, J., Cordiner, J., Cousin, S., & Earley, P. (2019). Understanding the middle tier: Comparative costs of academy and LA-maintained school systems. https://dx.doi.org/10.13140/RG.2.2.23574.29761
  17. Capp, M. J. (2017). The effectiveness of Universal Design for Learning: A meta-analysis of literature between 2013 and 2016. International Journal of Inclusive Education, 21(8), 791–807. https://dx.doi.org/10.1080/13603116.2017.1325074
  18. Capp, M. J. (2020). Teacher confidence to implement the principles, guidelines, and checkpoints of Universal Design for Learning. International Journal of Inclusive Education, 24(7), 706–720. https://dx.doi.org/10.1080/13603116.2018.1482014
  19. CAST. (2018). Universal Design for Learning Guidelines version 2.2. https://udlguidelines.cast.org
  20. Chen, X., Xie, H., & Hwang, G. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, 100005. https://dx.doi.org/10.1016/j.caeai.2020.100005
  21. Chick, J. C., Morello, L., Vance, J., Chick, J. C., Morello, L., & Vance, J. (2025). Universal Design for Learning as an equity framework: Addressing educational barriers and enablers for diverse non-traditional learners. Education Sciences, 15(9), 1265. https://dx.doi.org/10.3390/educsci15091265
  22. Chiu, T. K., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://dx.doi.org/10.1016/j.caeai.2022.100118
  23. Cliff, A., Walji, S., Jancic Mogliacci, R., Morris, N., & Ivancheva, M. (2022). Unbundling and higher education curriculum: A Cultural-Historical Activity Theory view of process. Teaching in Higher Education, 27(2), 217–232. https://dx.doi.org/10.1080/13562517.2019.1711050
  24. Coffman, S., & Draper, C. (2022). Universal Design for Learning in higher education: A concept analysis. Teaching and Learning in Nursing, 17(1), 36–41. https://dx.doi.org/10.1016/j.teln.2021.07.009
  25. Colasante, M. (2024). Five methodological dilemmas when implementing an Activity Theory transformative intervention in higher education. Teaching in Higher Education, 29(7), 1736–1756. https://dx.doi.org/10.1080/13562517.2024.2367655
  26. De Novais, A. S., Matelli, J. A., & Silva, M. B. (2024). Fuzzy soft skills assessment through active learning sessions. International Journal of Artificial Intelligence in Education, 34, 416–451. https://dx.doi.org/10.1007/s40593-023-00332-7
  27. Dickinson, K. J., & Gronseth, S. L. (2020). Application of Universal Design for Learning (UDL) principles to surgical education during the COVID-19 pandemic. Journal of Surgical Education, 77(5), 1008–1012. https://dx.doi.org/10.1016/j.jsurg.2020.06.005
  28. Diny Sujannah, W., Swastikawara, S., & Ningsih, Z. (2025). Advancing inclusive practices in higher education: Insights from Indonesia’s approach to disability support. Cogent Social Sciences, 11(1), 2593559. https://dx.doi.org/10.1080/23311886.2025.2593559
  29. DOAJ. (2025). Directory of Open Access Journals. https://doaj.org
  30. Doyle, A. J., Cassidy, D., & Condron, C. M. (2025). Universal Design for Learning (UDL) in simulation-based health professions education. Advances in Simulation, 10, 33. https://dx.doi.org/10.1186/s41077-025-00361-3
  31. Flick, U. (2018). An introduction to qualitative research (6th ed.). SAGE Publications.
  32. Florian, L. (2021). The universal value of teacher education for inclusive education. In A. Köpfer, J. J. W. Powell, & R. Zahnd (Eds.), International handbook of inclusive education: Global, national and local perspectives (pp. 89–106). Verlag Barbara Budrich. https://dx.doi.org/10.2307/j.ctv1f70kvj.8
  33. Gao, Y., Jia, Z., Zhao, L., & Han, S. (2023). The effect of activity participation in middle-aged and older people on the trajectory of depression in later life: National cohort study. JMIR Public Health and Surveillance, 9, e44682. https://dx.doi.org/10.2196/44682
  34. Garraway, J., Cupido, X., Dippenaar, H., Mntuyedwa, V., Ndlovu, N., Pinto, A., & Purcell van Graan, J. (2023). The Change Laboratory as an approach to harnessing conversation for academic development. International Journal for Academic Development, 28(3), 362–377. https://dx.doi.org/10.1080/1360144X.2021.2019039
  35. Gilleran Stephens, C., Antwi, S. H., & Linnane, S. (2025). Universal Design for Learning (UDL): A framework for re-design of an environmental education (EE) outreach program for a more inclusive and impactful science festival event. Discover Education, 4, 217. https://dx.doi.org/10.1007/s44217-025-00660-x
  36. Google LLC. (2024). Google Scholar. https://scholar.google.com
  37. Griful-Freixenet, J., Struyven, K., & Vantieghem, W. (2021). Toward more inclusive education: An empirical test of the Universal Design for Learning conceptual model among preservice teachers. Journal of Teacher Education, 72(4), 381–395. https://dx.doi.org/10.1177/0022487120965525
  38. Hallahan, D. P., Pullen, P. C., Kauffman, J. M., & Badar, J. (2020). Exceptional learners. In Oxford research encyclopedia of education. Oxford University Press. https://dx.doi.org/10.1093/acrefore/9780190264093.013.926
  39. Han, C., & Lei, J. (2024). Teachers’ and students’ beliefs towards Universal Design for Learning framework: A scoping review. SAGE Open, 14(3). https://dx.doi.org/10.1177/21582440241272032
  40. Hariyanto, H., Kristianingsih, F. X. D., & Maharani, R. (2025). Artificial intelligence in adaptive education: A systematic review of techniques for personalized learning. Discover Education, 4, 458. https://dx.doi.org/10.1007/s44217-025-00908-6
  41. Hennekeuser, D., Vaziri, D. D., Golchinfar, D., Schreiber, D., & Stevens, G. (2025). Enlarged education-exploring the use of generative AI to support lecturing in higher education. International Journal of Artificial Intelligence in Education, 35(3), 1096–1128. https://dx.doi.org/10.1007/s40593-024-00424-y
  42. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
  43. Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. https://dx.doi.org/10.1111/ejed.12533
  44. Hooper, K., & Lunn, S. J. (2024). Values in education: Exploration of artificial intelligence ethics syllabi using natural language processing analyses. In 2024 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE. https://dx.doi.org/10.1109/FIE61694.2024.10893167
  45. Hornby, G., & Kauffman, J. M. (2024). Inclusive education, intellectual disabilities and the demise of full inclusion. Journal of Intelligence, 12(2), 20. https://dx.doi.org/10.3390/jintelligence12020020
  46. Iweuno, B. N., Orekha, P., Ojediran, O., Imohimi, E., & Adu-Twum, H. T. (2024). Leveraging artificial intelligence for an inclusive and diversified curriculum. World Journal of Advanced Research and Reviews, 23(2), 1579–1590. https://dx.doi.org/10.30574/wjarr.2024.23.2.2440
  47. Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1–2), 18–26. https://dx.doi.org/10.1007/s13162-020-00161-0
  48. Jones, O., & Gatrell, C. (2014). Editorial: The future of writing and reviewing for IJMR. International Journal of Management Reviews, 16(3), 249–264. https://dx.doi.org/10.1111/ijmr.12038
  49. Kefallinou, A., Symeonidou, S., & Meijer, C. J. W. (2020). Understanding the value of inclusive education and its implementation: A review of the literature. Prospects, 49(3–4), 135–152. https://dx.doi.org/10.1007/s11125-020-09500-2
  50. Kim, K., Kwon, K., Ottenbreit-Leftwich, A., Bae, H., & Glazewski, K. (2023). Exploring middle school students’ common naive conceptions of artificial intelligence concepts, and the evolution of these ideas. Education and Information Technologies, 28(8), 9827–9854. https://dx.doi.org/10.1007/s10639-023-11600-3
  51. King-Sears, M. E., Stefanidis, A., Evmenova, A. S., Rao, K., Mergen, R. L., Owen, L. S., & Strimel, M. M. (2023). Achievement of learners receiving UDL instruction: A meta-analysis. Teaching and Teacher Education, 122, 103956. https://dx.doi.org/10.1016/j.tate.2022.103956
  52. Klimova, B., & Pikhart, M. (2025). Exploring the effects of artificial intelligence on student and academic well-being in higher education: A mini review. Frontiers in Psychology, 16, 1498132. https://dx.doi.org/10.3389/fpsyg.2025.1498132
  53. Liang, J., Stephens, J. M., & Brown, G. T. (2025). A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment. Frontiers in Education, 10, 1522841. https://dx.doi.org/10.3389/feduc.2025.1522841
  54. Li, J., Yan, Y., & Zeng, X. (2025). Exploring artificial intelligence in inclusive education: A systematic review of empirical studies. Applied Sciences, 15(23), 12624. https://dx.doi.org/10.3390/app152312624
  55. Losberg, J., & Zwozdiak-Myers, P. (2024). Inclusive pedagogy through the lens of primary teachers and teaching assistants in England. International Journal of Inclusive Education, 28(4), 402–422. https://dx.doi.org/10.1080/13603116.2021.1946722
  56. Mariam, G., Adil, L., & Zakaria, B. (2024). The integration of artificial intelligence (AI) into education systems and its impact on the governance of higher education institutions. International Journal of Professional Business Review, 9(12), e05176. https://dx.doi.org/10.26668/businessreview/2024.v9i12.5176
  57. McKenzie, J. A., & Dalton, E. M. (2020). Universal Design for Learning in inclusive education policy in South Africa. African Journal of Disability, 9, 776. https://dx.doi.org/10.4102/ajod.v9i0.776
  58. McMahon, D., & Tucker, L. (2024). Say yes to address AT: Create a new assistive technology culture informed by new AT guidance. TEACHING Exceptional Children, 57(1), 88–93. https://dx.doi.org/10.1177/00400599241282123
  59. McNicholl, A., Casey, H., Desmond, D., & Gallagher, P. (2021). The impact of assistive technology use for students with disabilities in higher education: A systematic review. Disability and Rehabilitation: Assistive Technology, 16(2), 130–143. https://dx.doi.org/10.1080/17483107.2019.1642395
  60. Melo-López, A., Basantes-Andrade, A., Gudiño-Mejía, B., & Hernández-Martínez, E. (2025). The impact of artificial intelligence on inclusive education: A systematic review. Education Sciences, 15(5), 539. https://dx.doi.org/10.3390/educsci15050539
  61. Mendoza, M. E. (2025). Supporting the inclusion of students with disabilities in higher education and vocational formation institutions. Journal of Further and Higher Education, 49(10), 1438–1453. https://dx.doi.org/10.1080/0309877X.2025.2574389
  62. Messinger-Willman, J., & Marino, M. T. (2010). Universal Design for Learning and assistive technology: Leadership considerations for promoting inclusive education in today’s secondary schools. NASSP Bulletin, 94(1), 5–16. https://dx.doi.org/10.1177/0192636510371977
  63. Mitchell, J., O’Loghlen, J., Bray, E., McLennan, V., McLeod, B., Brough, R., Hogana, C., Geraghtya, T., & Ownsworth, T. (2025). Assistive technology at work: A metasynthesis of the perspectives of people with spinal cord injury/damage. Disability and Rehabilitation: Assistive Technology, 20(6), 1596–1615. https://dx.doi.org/10.1080/17483107.2025.2475151
  64. Morse, J. M., & Mitcham, C. (2002). Exploring qualitatively-derived concepts: Inductive-deductive pitfalls. International Journal of Qualitative Methods, 1(4), 28–35. https://dx.doi.org/10.1177/160940690200100404
  65. Naeem, M., & Ozuem, W. (2022). Understanding the different types of UGC participants and social context for fashion brands: Insights from social media platforms. Qualitative Market Research: An International Journal, 25(2), 181–204. https://dx.doi.org/10.1108/QMR-02-2021-0028
  66. Naeem, M., Ozuem, W., Howell, K., & Ranfagni, S. (2023). A step-by-step process of thematic analysis to develop a conceptual model in qualitative research. International Journal of Qualitative Methods, 22. https://dx.doi.org/10.1177/16094069231205789
  67. Nagy, M., & Molontay, R. (2024). Interpretable dropout prediction: Towards XAI-based personalized intervention. International Journal of Artificial Intelligence in Education, 34(2), 274–300. https://dx.doi.org/10.1007/s40593-023-00331-8
  68. Ndlovu, S. (2021). Provision of assistive technology for students with disabilities in South African higher education. International Journal of Environmental Research and Public Health, 18(8), 3892. https://dx.doi.org/10.3390/ijerph18083892
  69. Phillips, T. M., Saleh, A., & Ozogul, G. (2023). An AI toolkit to support teacher reflection. International Journal of Artificial Intelligence in Education, 33(3), 635–658. https://dx.doi.org/10.1007/s40593-022-00295-1
  70. Rahim, N., Mokmin, N. A. M., & Wang, J. (2025). Empowering students with dysgraphia: The educational benefits of assistive technologies. Disability and Rehabilitation: Assistive Technology, 20(7), 2025–2036. https://dx.doi.org/10.1080/17483107.2025.2493737
  71. Rapp, A. C., & Corral-Granados, A. (2024). Understanding inclusive education—A theoretical contribution from system theory and the constructionist perspective. International Journal of Inclusive Education, 28(4), 423–439. https://dx.doi.org/10.1080/13603116.2021.1946725
  72. Rodríguez Torres, E., Comas Rodríguez, R., & Tovar Briñez, E. (2023). Use of AI to improve the teaching-learning process in children with special abilities. LatIA, 1(21), 81–97. https://dx.doi.org/10.62486/latia202321
  73. Roldán, S. M., Marauri, J., Aubert, A., & Flecha, R. (2021). How inclusive interactive learning environments benefit students without special needs. Frontiers in Psychology, 12, 661427. https://dx.doi.org/10.3389/fpsyg.2021.661427
  74. Roski, M., Sebastian, R., Ewerth, R., Hoppe, A., & Nehring, A. (2024). Learning analytics and the Universal Design for Learning (UDL): A clustering approach. Computers & Education, 214, 105028. https://dx.doi.org/10.1016/j.compedu.2024.105028
  75. Salas-Pilco, S. Z., Xiao, K., & Oshima, J. (2022). Artificial intelligence and new technologies in inclusive education for minority students: A systematic review. Sustainability, 14(20), 13572. https://dx.doi.org/10.3390/su142013572
  76. Saldana, J. (2021). The coding manual for qualitative researchers (4th ed.). SAGE Publications.
  77. Săseanu, A. S., Gogonea, R. M., & Ghiță, S. I. (2024). The social impact of using artificial intelligence in education. Amfiteatru Economic, 26(65), 89–105. https://dx.doi.org/10.24818/EA/2024/65/89
  78. Shireesha, M., & Jeevan, J. (2024). The role of artificial intelligence in personalized learning: A pathway to inclusive education. Library Progress International, 44(2), 1784–1795. https://dx.doi.org/10.48165/bapas.2024.44.2.1
  79. Song, Y., Weisberg, L. R., Zhang, S., Tian, X., Boyer, K. E., & Israel, M. (2024). A framework for inclusive AI learning design for diverse learners. Computers and Education: Artificial Intelligence, 6, 100212. https://dx.doi.org/10.1016/j.caeai.2024.100212
  80. St Clair-Sullivan, N., Brighton, L. J., Jha, P., Bone, A. E., Sudirman, N. A., Maddocks, M., & Bayly, J. (2025). Assistive technologies for people with advanced disease and in palliative care: A scoping review. Disability and Rehabilitation: Assistive Technology. Advance online publication. https://dx.doi.org/10.1080/17483107.2025.2536175
  81. UNESCO. (1994). The Salamanca statement and framework for action on special needs education. UNESCO.
  82. UNESCO. (2020). Inclusion and education: All means all. UNESCO Publishing.
  83. U.S. Department of Education, Institute of Education Sciences. (2023). Education Resources Information Center (ERIC). https://eric.ed.gov/
  84. Vaccarella, P., Goodman-Vincent, E., Cheng, H., & Cunningham, T. (2025). Barriers and facilitators of assistive technology use among adolescent students with learning disabilities: A mixed methods comparison of daily and less frequent users. Research in Developmental Disabilities, 165, 105088. https://dx.doi.org/10.1016/j.ridd.2025.105088
  85. Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), 343. https://dx.doi.org/10.3390/educsci15030343
  86. Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://dx.doi.org/10.1016/j.eswa.2024.124167
  87. Wang, S., & Sitthiworachart, J. (2025). Enhancing preservice teacher education: Integrating assistive technology and UDL within the TPACK framework: A systematic literature review. Edelweiss Applied Science and Technology, 9(4), 2706–2721. https://dx.doi.org/10.55214/25768484.v9i4.6642
  88. Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://dx.doi.org/10.1080/17439884.2020.1798995
  89. Wiltshire, G., & Ronkainen, N. (2021). A realist approach to thematic analysis: Making sense of qualitative data through experiential, inferential and dispositional themes. Journal of Critical Realism, 20(2), 159–180. https://dx.doi.org/10.1080/14767430.2021.1894909
  90. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://dx.doi.org/10.1186/s41239-019-0171-0. 
Article Preview

Quick Links

For Authors

Journal Policies

Journal Metrics

  • ISSN: 3108-0812
  • Frequency: Monthly
  • Peer Review: Double-blind
  • Access: Open Access
  • Indexing: CrossRef
  • Language: English

Contact Information

Editorial Office:
info@jcrelc.com

Visit Website:
jcrelc.com

Chat Now:
+91-7665235235

Quick Actions
Download PDF View References
Article Info
Pages
01-11