INNOVATION IN ART EDUCATION THROUGH ADAPTIVE AND DIGITAL LEARNING PLATFORMS

Aswitha V; Shalini E; Rajashri CK; Snehaa G; Subbulakshmi Packirisamy; Doris Ifeoma Ogeri

doi:10.29121/shodhkosh.v7.i3s.2026.7320

Authors

Aswitha V Assistant Professor, Department of English, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research
Shalini E Department of Computer Science, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research
Rajashri CK Assistant Professor, Department of Computer Science, Meenakshi College of Arts and Science, Meenakshi Academy of Higher Education and Research
Snehaa G Meenakshi College of Physiotherapy, Meenakshi Academy of Higher Education and Research
Subbulakshmi Packirisamy Assistant Professor, Department of Pharmacology, Meenakshi Ammal Dental College and Hospital, Meenakshi Academy of Higher Education and Research
Doris Ifeoma Ogeri Faculty of Management, Shinawatra University, Thailand; Research Fellow, INTI International University, Malaysia

DOI:

https://doi.org/10.29121/shodhkosh.v7.i3s.2026.7320

Keywords:

Adaptive Learning Platforms, Digital Art Education, Artificial Intelligence In Education, Virtual And Augmented Reality, Personalized Learning Systems, Creative Digital Learning

Abstract [English]

The use of digital tools and adaptive learning systems has changed the modern art education greatly by making it flexible, personalized, and interactive. Conventional in person training is usually constrained in terms of accessibility, personal feedback and resources. Adaptive digital learning systems solve these issues by means of applying artificial intelligence and data analytics and interactive multimedia tools to customize learning material and content based on artistic talents, learning speeds, and creativity that the students have. This paper discusses the theoretical and technological underpinnings of adaptive online platforms in art education as well as its educational implications. It investigates the use of virtual studios, digital drawing instruments, augmented reality (AR), virtual reality (VR), and collaborative multimedia space in improving artistic experimentation and creativity. The study also examines the algorithms of personalization, AI-informed feedback, and adaptive evaluation models that facilitate the process of unceasing enhancement of artistic learning outcomes. Findings suggest that adaptive online platforms can equally contribute to creativity and engagement in addition to increasing accessibility to a wide range of learners through flexible learning opportunities and inclusive learning materials.

References

Chen, S., Lin, P., and Chien, W. (2022). Children’s Digital Art Ability Training System Based on AI-Assisted Learning: A Case Study of Drawing Color Perception. Frontiers in Psychology, 13, Article 102931. https://doi.org/10.3389/fpsyg.2022.823078 DOI: https://doi.org/10.3389/fpsyg.2022.823078

Cong, S. (2024). A Study of Teaching Strategies Optimized with the Integration of Artificial Intelligence Technologies. Applied Mathematics and Nonlinear Sciences, 9, 1195. https://doi.org/10.2478/amns-2024-1195 DOI: https://doi.org/10.2478/amns-2024-1195

Dai, Y., Liu, A., Qin, J., Guo, Y., Jong, M., Chai, C., and Lin, Z. (2022). Collaborative Construction of Artificial Intelligence Curriculum in Primary Schools. Journal of Engineering Education, 112, 23–42. https://doi.org/10.1002/jee.20503 DOI: https://doi.org/10.1002/jee.20503

Engelsrud, G., Rugseth, G., and Nordtug, B. (2021). Taking Time for New Ideas: Learning Qualitative Research Methods in Higher Sports Education. Sport, Education and Society, 28, 239–252. https://doi.org/10.1080/13573322.2021.2014804 DOI: https://doi.org/10.1080/13573322.2021.2014804

Ezquerra, Á., Agen, F., Rodríguez-Arteche, I., and Ezquerra-Romano, I. (2022). Integrating Artificial Intelligence into Research on Emotions and Behaviors in Science Education. Eurasia Journal of Mathematics, Science and Technology Education, 18, Article 11927. https://doi.org/10.29333/ejmste/11927 DOI: https://doi.org/10.29333/ejmste/11927

Freedman, K., Cornwall, J., Schulte, C., Carpenter, B., and Castro, J. (2022). Mapping Research with a Systematic Review: The Example of Social and Emotional Learning in Art Education. Studies in Art Education, 63, 296–312. https://doi.org/10.1080/00393541.2022.2116679 DOI: https://doi.org/10.1080/00393541.2022.2116679

Jadhav, K. D. (2027). Integrating IoT, Sensors, and Machine Learning for Enhancing Crop Yield and Irrigation Efficiency Systems. In R. R. Janghel, R. Doriya, J. Lachure, and Y. K. Rathore (Eds.), Sustainable Agriculture Production Using Blockchain Technology. https://doi.org/10.1002/9781394248711.ch11 DOI: https://doi.org/10.1002/9781394248711.ch11

Kahila, J., Vartiainen, H., Tedre, M., Arkko, E., Lin, A., Pope, N., Jormanainen, I., and Valtonen, T. (2024). Pedagogical Framework for Cultivating Children's Data Agency and Creative Abilities in the Age of AI. Informatics in Education, 23, Article 15. https://doi.org/10.15388/infedu.2024.15 DOI: https://doi.org/10.15388/infedu.2024.15

Karthikeyan, J., Vasanthan, R., Sundari, P. S., Nandhini, T. J., and Devi, V. C. (2023). Construction and Implementation of English Translation Simulation Training Classroom Based on Deep Learning. In 2023 2nd International Conference on Smart Technologies for Smart Nation (SmartTechCon) ( 716–719). https://doi.org/10.1109/SmartTechCon57526.2023.10391666 DOI: https://doi.org/10.1109/SmartTechCon57526.2023.10391666

Karule, K. P., Sapkal, V., Mirajkar, G., Babajanov, M., Tajne, P., Ahire, L. K., and Qushnazarova, U. (2025). Leveraging Transfer Learning Techniques for Automated Tuberculosis Classification on Chest X-ray. Indian Journal of Tuberculosis, 72, S512–S530. https://doi.org/10.1016/j.ijtb.2025.11.015 DOI: https://doi.org/10.1016/j.ijtb.2025.11.015

Kashyap, S. V., Purohit, S., Kumar, D. A., Jawaid, F. I. M., Kumar, J. R. R., and Ajani, S. N. (2025). Visual Storytelling and Explainable Intelligence in Organizational Change Communication. ShodhKosh: Journal of Visual and Performing Arts, 6(5s), 696–707. https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6965 DOI: https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6965

Khoirroni, I. A., Patinasarani, R., Hermayanti, N. I., and Santoso, G. (2023). Character Education: Elementary School Children in the Digital Age (Pendidikan karakter: Tingkat Anak Sekolah Dasar Di Era Digital). Jurnal Pendidikan Transformasi, 2, 269–279.

Lee, H., and Lee, M. (2021). Visual Art Education and Social-Emotional Learning of Students in Rural Kenya. International Journal of Educational Research, 108, Article 101781. https://doi.org/10.1016/j.ijer.2021.101781 DOI: https://doi.org/10.1016/j.ijer.2021.101781

More, S. S., and Birmule, P. R. (2025). Technology-Based Scheduling and Event Management in Sports. International Journal of Research and Development in Management Research, 14(1), 50–54. https://doi.org/10.65521/ijrdmr.v14i1.288 DOI: https://doi.org/10.65521/ijrdmr.v14i1.288

Patil, R. V., et al. (2025). Decentralized Autonomous Organizations as Emerging Economic Entities in Accounting and Governance Frameworks. International Journal of Accounting and Economics Studies, 12(4), 166–177. https://doi.org/10.14419/1sy2j677 DOI: https://doi.org/10.14419/1sy2j677

Rawandale, U. S., and Kolte, M. T. (2021). Design, Development and Analysis of Variable Bandwidth Filter Bank for Enhancing the Performance of Hearing Aid System. International Journal of Intelligent Engineering Systems, 14(6), 391–401. https://doi.org/10.22266/ijies2021.1231.35 DOI: https://doi.org/10.22266/ijies2021.1231.35

Rizzuto, T., Cordeiro, K., and Roda, A. (2022). The Lost Art: Teachers’ Perceptions of the Connections Between the Arts and Social-Emotional Learning. Arts Education Policy Review, 125, 150–162. https://doi.org/10.1080/10632913.2022.2053919 DOI: https://doi.org/10.1080/10632913.2022.2053919

Seo, K., Tang, J., Roll, I., Fels, S., and Yoon, D. (2021). The Impact of Artificial Intelligence on Learner–Instructor Interaction in Online Learning. International Journal of Educational Technology in Higher Education, 18, Article 54. https://doi.org/10.1186/s41239-021-00292-9 DOI: https://doi.org/10.1186/s41239-021-00292-9

Vaidya, C., Chopade, S., Khobragade, P., Bhure, K., and Parate, A. (2025). Development of a Face Recognition-Based Attendance System. In 2025 12th International Conference on Emerging Trends in Engineering and Technology – Signal and Information Processing (ICETET-SIP) ( 1–6). https://doi.org/10.1109/ICETETSIP64213.2025.11156883 DOI: https://doi.org/10.1109/ICETETSIP64213.2025.11156883

Venkata, S. B., Wong, C. H., Karwande, V. S., Divyaraj, G., Singh, S., and Patil, R. V. (2025). Metaheuristic-Tuned GraMNet Architecture for Enhanced Video-Based Anomaly Detection Using UCF50 Dataset. In International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3 2025). https://doi.org/10.1109/ISAC364032.2025.11156508 DOI: https://doi.org/10.1109/ISAC364032.2025.11156508

Vistorte, A., Deroncele-Acosta, A., Ayala, J., Barrasa, A., López-Granero, C., and Martí-González, M. (2024). Integrating Artificial Intelligence to Assess Emotions in Learning Environments: A Systematic Literature Review. Frontiers in Psychology, 15, Article 1387089. https://doi.org/10.3389/fpsyg.2024.1387089 DOI: https://doi.org/10.3389/fpsyg.2024.1387089