Artificial Intelligence (AI) Applications in Personalized Learning for Autistic Students: A Systematic Literature Review

Personalized learning is a significant approach in the education of autistic students due to the diversity of their learning profiles, particularly in terms of social communication, interaction, and sensory sensitivity. However, the optimal implementation of personalized learning in classrooms continues to face multiple constraints, including teacher workload and challenges in providing interventions that are truly individualized on a consistent basis. While Artificial Intelligence (AI) technology is seen as having the potential to support this process through more adaptive applications, existing literature on its implementation remains varied, fragmented, and highlights research gaps. Therefore, this study was conducted as a Systematic Literature Review (SLR) with the objectives of identifying the types of AI instruments commonly used, assessing their impact on social communication, emotion management, and student engagement, and examining the challenges and research gaps in the use of AI within special education. The study employed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and searched six major databases: Scopus, Web of Science (WoS), ERIC, IEEE Xplore, ACM Digital Library, and Google Scholar. From a total of 226 identified articles, 24 publications from 2019 to 2026 met the inclusion criteria and were analyzed thematically. The findings indicate that the most frequently used AI instruments include Intelligent Tutoring Systems (ITS), social robots such as NAO, adaptive learning systems, and emotion recognition technologies. The use of these technologies has been shown to positively impact student focus and engagement, support social communication, and assist in emotion management through personalized activities. Nevertheless, the implementation of AI interventions also faces major challenges, including technical constraints, high training demands for teachers, and concerns regarding data privacy and ethics. Accordingly, this study recommends that AI should be employed as a pedagogical support tool, integrated with human interaction and professional teacher training. The findings are expected to provide meaningful contributions to inclusive pedagogical practices and to the development of safer, more equitable educational technology policies that respect and accommodate the neurodiversity of autistic students.

Artificial Intelligence (AI), Personalized Learning

1. Abu-Amara, F., Mohammad, H., & Bensefia, A. (2024). Robot-based therapy for improving academic skills of children with autism. International Journal of Information Technology (Singapore), 16(6), 3371–3380. https://doi.org/10.1007/s41870-024-01883-1 [Google Scholar] [Crossref]

2. Adako, O. P., Adeusi, O. C., & Alaba, P. A. (2024). Revolutionizing autism education: Harnessing AI for tailored skill development in social, emotional, and independent learning domains. Journal of Computational and Cognitive Engineering, 3(4), 348–359. https://doi.org/10.47852/bonviewJCCE42023414 [Google Scholar] [Crossref]

3. Anagnostopoulou, P., Alexandropoulou, V., Lorentzou, G., Lykothanasi, A., Ntaountaki, P., & Drigas, A. (2020). Artificial Intelligence in Autism Assessment. International Journal of Emerging Technologies in Learning, 15(06). https://online-journals.org/index.php/i-jet/article/view/11231/6727 [Google Scholar] [Crossref]

4. Anwar, M., Karsidi, R., Sunardi, & Widyastono, H. (2026). The effects of universal design for learning and AI-AT on the engagement and academic outcomes of students with disabilities. International Journal of Learning, Teaching and Educational Research, 25(3), 557–581. https://doi.org/10.26803/ijlter.25.3.24 [Google Scholar] [Crossref]

5. Atturu, H., Naraganti, S., & Rao, B. R. (2025). Effectiveness of artificial intelligence-based platform in administering therapies for children with autism spectrum disorder: 12-month observational study. JMIR Neurotechnology, 4, Article e70589. https://doi.org/10.2196/70589 [Google Scholar] [Crossref]

6. Barua, P. D., Vicnesh, J., Gururajan, R., Oh, S. L., Palmer, E., Azizan, M. M., Kadri, N. A., & Acharya, U. R. (2022). Artificial intelligence enabled personalised assistive tools to enhance education of children with neurodevelopmental disorders: A review. International Journal of Environmental Research and Public Health, 19(3), 1192. https://doi.org/10.3390/ijerph19031192 [Google Scholar] [Crossref]

7. Clabaugh, C., et al. (2019). Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders. Frontiers in Robotics and AI. https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2019.00110/full [Google Scholar] [Crossref]

8. Clarke, V., & Braun, V. (2013). Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist, 26(2), 77–101. [Google Scholar] [Crossref]

9. Daud, N., Ghazali, J. M., & Ahmad, S. N. (2025). A review of AI-driven approaches in autism education. Malaysian Journal of Information and Communication Technology, 10(2), 167–186. https://doi.org/10.53840/myjict10-2-232 [Google Scholar] [Crossref]

10. Devadas, R. S. (2025). AI augmented education for children with special needs: A scholarly perspective. World Journal of Advanced Engineering Technology and Sciences, 15(3), 2000–2008. https://doi.org/10.30574/wjaets.2025.15.3.1125 [Google Scholar] [Crossref]

11. Frolli, A., Cavallaro, A., La Penna, I., Sica, S. L., & Bloisi, D. (2024). Artificial intelligence and autism spectrum disorders: A new perspective on learning. Proceedings of the Digital Innovations for Learning and Neurodevelopmental Disorders. [Google Scholar] [Crossref]

12. Gadzhimusieva, D., Meliá, S., Lledó, G. L., & Nasabeh, S. S. (2026). Development and pilot evaluation of an AI-driven learning management system for personalized education for autistic students. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13888-9 [Google Scholar] [Crossref]

13. Haque, S., Islam, M. S., Islam, M. I., Islam, M. S., Khan, R., Tarafder, M. T. R., & Mohammad, N. (2025). Enhancing adaptive learning, communication, and therapeutic accessibility through the integration of artificial intelligence and data-driven personalization in digital health platforms for students with autism spectrum disorder. Journal of Posthumanism, 5(8), 737–756. https://doi.org/10.63332/joph.v5i8.3255 [Google Scholar] [Crossref]

14. Kotsi, S., Handrinou, S., Iatraki, G., & Soulis, S. G. (2025). A review of artificial intelligence interventions for students with autism spectrum disorder. Disabilities, 5(1), 7. https://doi.org/10.3390/disabilities5010007 [Google Scholar] [Crossref]

15. Kouroupa, A., Laws, K. R., Irvine, K., Mengoni, S. E., Baird, A., & Sharma, S. (2022). The use of social robots with children and young people on the autism spectrum: A systematic review and meta-analysis. PLOS ONE, 17(6), e0269800. https://doi.org/10.1371/journal.pone.0269800 [Google Scholar] [Crossref]

16. La Fauci De Leo, A., Bagheri Zadeh, P., Voderhobli, K., & Akbari, A. S. (2025). A new AI framework to support social-emotional skills and emotion awareness in children with autism spectrum disorder. Computers, 14(7), 292. https://doi.org/10.3390/computers14070292 [Google Scholar] [Crossref]

17. Li, G., Zarei, M. A., Alibakhshi, G., & Labbaf, A. (2024). Teachers and educators’ experiences and perceptions of artificial-powered interventions for autism groups. BMC Psychology, 12, 199. https://doi.org/10.1186/s40359-024-01664-2 [Google Scholar] [Crossref]

18. Marino, F., et al. (2020). Outcomes of a Robot-Assisted Social-Emotional Understanding Intervention for Young Children with Autism Spectrum Disorders. Journal of Autism and Developmental Disorders. https://www.researchgate.net/publication/331634698_Outcomes_of_a_Robot-Assisted_Social-Emotional_Understanding_Intervention_for_Young_Children_with_Autism_Spectrum_Disorders [Google Scholar] [Crossref]

19. Nair, A. B., Madhuvanthi, M., & Sathyamurthi, K. (2025). Effectiveness of AI-based learning tools for autistic children: A study on special educators in Chennai. GAP Bodhi Taru: A Global Journal of Humanities, 8, 83–89. [Google Scholar] [Crossref]

20. Nasir, H. M., Brahin, N. M. A., Zainuddin, S., Isa, I. S. M., Azhar, M. A., Syazwani, N., & Jamal, N. (2025). AI-powered mobile application ‘Play Home’ for autism education. International Journal of Research and Innovation in Social Science, 9(IIIS), 6618–6626. [Google Scholar] [Crossref]

21. Pontillo, A. L., & Oliva, P. (2025). Artificial intelligence, universal design for learning, and adaptive learning for students with specific learning disorders: A possible synergy? Giornale Italiano di Educazione alla Salute, Sport e Didattica Inclusiva, 9(2). https://doi.org/10.32043/gsd.v9i2.1302 [Google Scholar] [Crossref]

22. Rofidi, M. R. Z., Asmai, S. A., Fauadi, M. H. F. M., Masdzarif, N. D. I., & Jaya, A. S. M. (2025). AISTY: An explainable AI-driven vision-based adaptive learning system for children with autism spectrum disorder. International Journal of Research and Innovation in Social Science, 9(10), 4777–4783. [Google Scholar] [Crossref]

23. Singh, S., & Wadhwa, P. (2025). Artificial intelligence for implementing universal design for learning in inclusive education. International Journal of Social Impact, 10(4), 58–68. https://doi.org/10.25215/2455/1004706 [Google Scholar] [Crossref]

24. Wang, J. (2026). The impact of artificial intelligence-based personalized learning systems on the cognitive ability enhancement of children with special needs. International Journal of Cognitive Informatics and Natural Intelligence, 20(1). https://doi.org/10.4018/IJCINI.397824 [Google Scholar] [Crossref]

25. Yang, Q., Lu, H., Liang, D., Gong, S., & Feng, H. (2024). Surprising performances of students with autism in classroom with NAO robot. arXiv. http://arxiv.org/abs/2407.12014 [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study