AI FOR INCLUSIVE ART EDUCATION FOR DIFFERENTLY ABLED LEARNERS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6793Keywords:
Artificial Intelligence, Inclusive Education, Art Education, Differently Abled Learners, Universal Design for Learning (UDL), Adaptive LearningAbstract [English]
This paper examines the change potential within Artificial Intelligence (AI) in supporting the inclusion of the differently abled in learning art. As a form of expression, art is very vital in the development of creativity, emotional intelligence, and communication skills. The standard systems of art education do not however support the diversity of needs of students with physical, sensory or cognitive disabilities. Through the use of AI technologies, e.g., Image-to-audio conversion, gesture recognition, speech-to-text, and others, educators could design adaptive, accessible, and personal learning experiences. This study is based on the principles of Universal Design of Learning (UDL) and constructivist theories to explore the role of AI-driven tools in promoting participation and self-expression as well as engagement of learners with disabilities. The research clearly uses a mixed-method design, which involves both qualitative data gained in the process of interviews and observations and quantitative data analysis on the level of performance and engagement of learners. It also looks at such issues as accessibility, affordability, data privacy, as well as ethical issues in implementing AI technologies in education. The anticipated results are that there would be better inclusivity, increased learner autonomy, and equal access to art learning materials. Finally, the study will seek to show how AI can serve as an agent of social innovation, last, but not least: to fill in the gaps in educational equity and allow learners with disabilities to take part in creative processes to their full extent.
References
Adeleye, O. O., Eden, C. A., and Adeniyi, I. S. (2024). Innovative Teaching Methodologies in the Era of Artificial Intelligence: A Review of Inclusive Educational Practices. World Journal of Advanced Engineering Technology and Sciences, 11(2), 69–79. https://doi.org/10.30574/wjaets.2024.11.2.0091 DOI: https://doi.org/10.30574/wjaets.2024.11.2.0091
Anshu, A. (2025). Leveraging Internet of Things (IoT) to Enhance Accessibility and Independence for People with Disabilities. LatIA, 3, Article 114. https://doi.org/10.62486/latia2025114 DOI: https://doi.org/10.62486/latia2025114
Ayala, S. (2023). ChatGPT as a Universal Design for Learning Tool Supporting College Students with Disabilities. Educational Renaissance, 12, 22–41.
Ganesan, J., Azar, A., Alsenan, S., Kamal, N., Qureshi, B., and Hassanien, A. (2022). Deep Learning Reader for the Visually Impaired. Electronics, 11(20), Article 3335. https://doi.org/10.3390/electronics11203335 DOI: https://doi.org/10.3390/electronics11203335
Gupta, M., and Kaul, S. (2024). AI in Inclusive Education: A Systematic Review of Opportunities and Challenges in the Indian Context. MIER Journal of Educational Studies, Trends and Practices, 14(2), 429–461. https://doi.org/10.52634/mier/2024/v14/i2/2702 DOI: https://doi.org/10.52634/mier/2024/v14/i2/2702
Islam, M. Z., and Based, M. A. (2019). Speech Recognition System for Speech-to-Text and Text-to-Speech for Autistic Persons. Barrister Shameem Haider Patwary, 11, 86.
Jishnu, T., and Antony, A. (2024). LipNet: End-to-End Lipreading. Indian Journal of Data Mining, 4(1), 1–4. https://doi.org/10.54105/ijdm.A1632.04010524 DOI: https://doi.org/10.54105/ijdm.A1632.04010524
Khalil, M., Slade, S., and Prinsloo, P. (2024). Learning Analytics in Support of Inclusiveness and Disabled Students: A Systematic Review. Journal of Computer Assisted Learning in Higher Education, 36(2), 202–219. https://doi.org/10.1007/s12528-023-09363-4 DOI: https://doi.org/10.1007/s12528-023-09363-4
Kumar, L., Renuka, D., Rose, S., and Wartana, I. (2022). Deep Learning-Based Assistive Technology on Audiovisual Speech Recognition for the Hearing Impaired. International Journal of Cognitive Computing Engineering, 3, 24–30. https://doi.org/10.1016/j.ijcce.2022.01.003 DOI: https://doi.org/10.1016/j.ijcce.2022.01.003
Lestari, A. D. S., Murwani, F., Wardana, L., and Wati, A. (2024). Problems of Inclusive Learning in Fostering Entrepreneurial Motivation in Students with Disabilities: A Systematic Literature Review. Journal of Educational Analysis, 3(2), 161–180. https://doi.org/10.55927/jeda.v3i2.9246 DOI: https://doi.org/10.55927/jeda.v3i2.9246
Mulfari, D., Meoni, G., Marini, M., and Fanucci, L. (2021). Machine Learning Assistive Application for users with Speech Disorders. Applied Soft Computing, 103, Article 107147. https://doi.org/10.1016/j.asoc.2021.107147 DOI: https://doi.org/10.1016/j.asoc.2021.107147
Nahar, L., Sulaiman, R., and Jaafar, A. (2022). An Interactive Math Braille Learning Application to Assist Blind Students in Bangladesh. Assistive Technology, 34(2), 157–169. https://doi.org/10.1080/10400435.2020.1734112 DOI: https://doi.org/10.1080/10400435.2020.1734112
Navas-Bonilla, C. R., Guerra-Arango, J. A., Oviedo-Guado, D. A., and Murillo-Noriega, D. E. (2025). Inclusive Education Through Technology: A Systematic Review of Types, Tools and Characteristics. Frontiers in Education, 10, Article 1527851. https://doi.org/10.3389/feduc.2025.1527851 DOI: https://doi.org/10.3389/feduc.2025.1527851
Paul, S., Lakhani, D., Aryan, D., Das, S., and Varshney, R. (2024). Lip Reading System for Speech-Impaired Individuals. International Journal for Multidisciplinary Research, 6, Article IJFMR240218643. https://doi.org/10.36948/ijfmr.2024.v06i02.18643 DOI: https://doi.org/10.36948/ijfmr.2024.v06i02.18643
Ramya, M. M. (2024). Advancing Inclusive Learning Through Systematic AI Integration for Children with Disabilities. Innovative Research, 2, 6–10.
Swarnamba, S., and Revanna, B. (2024). Efficient Examination Method for Blind People Using MatlAB and Embedded Systems. International Journal of Engineering Research and Technology, 13, 1–4.
Udvaros, J., and Forman, N. (2023). Artificial Intelligence and Education 4.0. In Proceedings of the 17th International Technology, Education and Development Conference (6309–6317). Valencia, Spain. https://doi.org/10.21125/inted.2023.1670 DOI: https://doi.org/10.21125/inted.2023.1670
Vyapari, R. R., and Nimbhore, D. S. S. (2023). Marathi Isolated Speech Recognition for Diseases Using HTK in the Healthcare Sector. International Journal of Advanced Research in Engineering and Applied Sciences, 12, 1–17.
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Copyright (c) 2025 Mohd Faisal, Piyush Pal, Ms. Babitha B S, Himanshu Makhija, Kalpana Munjal, Abhinav Mishra, Vishal Ambhore

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