AI-DRIVEN DIGITAL STORYTELLING REDEFINING VISUAL NARRATIVES IN EDUCATION

Authors

  • Dr.Shikha Dubey Department of MCA, DPGU 's School of Management and Research, Pune , India
  • Dr. Vipul Vekariya Professor, Department of Computer science and Engineering, Faculty of Engineering and Technology, Parul institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India
  • Dr. Peeyush Kumar Gupta Assistant Professor, ISDI - School of Design & Innovation, ATLAS SkillTech University, Mumbai, Maharashtra, India
  • Ankit Punia Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
  • Manish Nagpal Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, Solan, 174103, India
  • Mohit Aggarwal School of Engineering & Technology, Noida International University, Uttar Pradesh 203201, India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6616

Keywords:

Artificial Intelligence (AI), Digital Storytelling, Visual Narratives, Educational Technology, Natural Language Generation, Emotion Analysis, Personalized Learning

Abstract [English]

Artificial Intelligence (AI) had become a radical force in digital storytelling and it was reshaping the way visual storytelling had been produced and distributed in the educational sector. Although it was becoming more popular, there was a gap in the literature on the most effective AI methods to achieve better engagement, understanding and creativity in the learner. This paper had explored three AI-inspired methods namely (1) Natural Language Generation (NLG) and Narrative Intelligence to create adaptive stories; (2) Computer Vision and Generative Visual Synthesis to generate dynamic visual narrative and (3) Multimodal Emotion and Engagement Analysis to create emotional adaptations in real-time. The techniques were now relatively appraised in regard to prime educational aspects, such as creativity, flexibility, emotional involvement, and retention of learning. Experimental findings had shown that, NLG had been better at narrative coherence and contextual relevance whereas, visual synthesis had been better in conceptual visualization as well as learner immersion. Nevertheless, the multimodal emotion-analysis approach had shown an overall better performance with an average of 28-percentage-point better performance in the engagement and comprehension than the other approaches.

References

Ginting, D., Woods, R. M., Barella, Y., Limanta, L. S., Madkur, A., and How, H. E. (2024). The Effects of Digital Storytelling on the Retention and Transferability of Student Knowledge. SAGE Open, 14(3). https://doi.org/10.1177/21582440241271267 DOI: https://doi.org/10.1177/21582440241271267

Gupta, N., et al. (2024). AI-Driven Digital Narratives: Revolutionizing Storytelling in Contemporary English Literature through Virtual Reality. In 2024 Second International Conference on Computational and Characterization Techniques in Engineering and Sciences (IC3TES) (pp. 1–5). IEEE. https://doi.org/10.1109/IC3TES62412.2024.10877467 DOI: https://doi.org/10.1109/IC3TES62412.2024.10877467

Hedaoo, M., Jambhulkar, S., Tagade, K., Mate, G., and Durugkar, P. (2025). A Comparative Study of Education Loan Scheme of Punjab National Bank and ICICI Bank, Nagpur 2021–2024. International Journal of Research and Development in Management Review, 14(1), 67–71. DOI: https://doi.org/10.65521/ijrdmr.v14i1.295

Kumbhar, A. S., Khot, A. S., and Kharade, K. G. (2025). Sentiment Analysis of Amazon Product Reviews. IJEECS, 14(1), 20–25.

Li, Y., et al. (2024). In-Situ Mode: Generative AI-Driven Characters Transforming Art Engagement through Anthropomorphic Narratives. arXiv preprint arXiv:2409.15769.

Naik, I., Naik, D., and Naik, N. (2023). Chat Generative Pre-Trained Transformer (ChatGPT): Comprehending its Operational Structure, AI Techniques, Working, Features and Limitations. In 2023 IEEE International Conference on ICT in Business Industry and Government (ICTBIG) (pp. 1–9). IEEE. https://doi.org/10.1109/ICTBIG59752.2023.10456201

Naik, I., Naik, D., and Naik, N. (2023). Chat Generative Pre-Trained Transformer (ChatGPT): Comprehending its Operational Structure, AI Techniques, Working, Features and Limitations. In 2023 IEEE International Conference on ICT in Business Industry and Government (ICTBIG) (pp. 1–9). IEEE. https://doi.org/10.1109/ICTBIG59752.2023.10456201 DOI: https://doi.org/10.1109/ICTBIG59752.2023.10456201

Nik, E., Gauci, R., Ross, B., and Tedeschi, J. (2024). Exploring the Potential of Digital Storytelling in a Widening Participation Context. Educational Research, 66(3), 329–346. https://doi.org/10.1080/00131881.2024.2362336

Nik, E., Gauci, R., Ross, B., and Tedeschi, J. (2024). Exploring the Potential of Digital Storytelling in a Widening Participation Context. Educational Research, 66(3), 329–346. https://doi.org/10.1080/00131881.2024.2362336 DOI: https://doi.org/10.1080/00131881.2024.2362336

Preetam, Muppalla, S. C. M., Raj, A., and Chawla, J. (2024). AI Narratives: Bridging Visual Content and Linguistic Expression. In 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE) (pp. 1–6). IEEE. https://doi.org/10.1109/ICSPCRE62303.2024.10675203

Preetam, S. C., Muppalla, A., Raj, A., and Chawla, J. (2024). AI Narratives : Bridging Visual Content and Linguistic Expression. In 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE) (pp. 1–6). IEEE. https://doi.org/10.1109/ICSPCRE62303.2024.10675203 DOI: https://doi.org/10.1109/ICSPCRE62303.2024.10675203

Quecan, L. (2021). Visual Aids Make a Big Impact on ESL Students: A Guidebook for ESL teachers.

Sato, T., Lai, Y., and Burden, T. (2022). The Role of Individual Factors in L2 Vocabulary Learning with Cognitive-Linguistics-Based Static and Dynamic Visual Aids. ReCALL, 34(2), 201–217. https://doi.org/10.1017/S0958344021000288 DOI: https://doi.org/10.1017/S0958344021000288

Yu, W., Peng, J., Li, J., Deng, Y., and Li, H. (2022). Application Research of Image Feature Recognition Algorithm in Visual Image Recognition. In 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) (pp. 812–816). IEEE. https://doi.org/10.1109/TOCS56154.2022.10016132

Yu, W., Peng, J., Li, J., Deng, Y., and Li, H. (2022). Application Research of Image Feature Recognition Algorithm in Visual Image Recognition. In 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) (pp. 812–816). IEEE. https://doi.org/10.1109/TOCS56154.2022.10016132 DOI: https://doi.org/10.1109/TOCS56154.2022.10016132

Zhang, Y., et al. (2025). AI-Driven Optimization Algorithm for Cultural Dissemination Pathways and its Educational Applications. In Proceedings of the 2025 International Conference on Artificial Intelligence and Educational Systems. Association for Computing Machinery. https://doi.org/10.1145/3744367.3744429 DOI: https://doi.org/10.1145/3744367.3744429

Downloads

Published

2025-12-10

How to Cite

Dubey, S., Vekariya, V., Gupta, P. K., Punia, A., Nagpal, M., & Aggarwal, M. (2025). AI-DRIVEN DIGITAL STORYTELLING REDEFINING VISUAL NARRATIVES IN EDUCATION. ShodhKosh: Journal of Visual and Performing Arts, 6(1s), 1–10. https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6616