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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM49423
Volume:
9 |
6 |
Jun 2025
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science and Engineering
Government College of Technology , Coimbatore,
Department of Computer Science and Engineering
Government College of Technology, Coimbatore, India
This study investigates the expanding role of generative artificial intelligence (AI) in shaping modern multimedia teaching tools. Advanced models like GPT-4, DALL·E, and Stable Diffusion now facilitate the automated creation of diverse educational content such as text, images, audio, and video. These multimodal materials are designed to engage learners across multiple sensory modalities, thereby reducing cognitive overload in alignment with cognitive load theory. A prominent case examined in this work is the use of generative AI to produce situational videos for Chinese poetry, where abstract poetic themes are rendered into visually compelling content reflecting traditional cultural aesthetics. This example highlights the potential of generative AI to foster immersive and emotionally engaging learning environments. The paper concludes that generative AI holds significant promise for transforming education through the scalable creation of personalized, multimodal instructional resources. Future progress in AI development and cross-disciplinary collaboration will be critical to realizing its full educational impact.
KEYWORDS
The integration of Generative AI in education has demonstrated immense potential in automating the creation of multimedia teaching aids. Through AI-driven text generation, image synthesis, and video production, learning materials become more engaging, interactive, and accessible. The case study on Tang poetry highlights AI’s ability to preserve cultural heritage while making complex literary works more comprehensible. However, challenges such as semantic misalignment, cultural authenticity, and content synchronization must be addressed. Future advancements should focus on enhancing AI’s contextual understanding, improving adaptive learning features
1 OpenAI.(2023). GPT-4 Technical Report.Retrieved from https://openai.com/research/gpt-4. 2 DeepSeek.ai Research Group. (2024). Adaptive AI in Education: A Case Study on Text-Based Learning Systems. Journal of Artificial Intelligence in Education, 12(3), 56-78. 3 Zhang, Y., & Liu, W. (2023). AI-Generated Assessments for Literary Studies: Enhancing Engagement and Learning Outcomes. Educational Technology & Society, 26(2), 112-129. 4 Ramesh, A., Pavlov, M., Goh, G., Gray, S., &Misra, V. (2021). DALL-E: Creating Images from Text Descriptions. Advances in Neural Information Processing Systems, 34, 12312-12324. 5 Ho, J., Jain, A., &Abbeel, P. (2022). Stable Diffusion for Artistic Representation: Applications in Digital Humanities. IEEE Transactions on Image Processing, 31(6), 2321-2334. 6 Li, P., &Xu, H. (2023). Multimodal AI in Digital Humanities: Exploring AI-Generated Art and Literature. AI & Society, 38(4), 921-940. 7 Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ...&Amodei, D. (2020). Language Models Are Few-Shot Learners. NeurIPS, 33, 1877-1901. 8 Sweller, J. (1988). Cognitive Load Theory in Learning: Implications for Instructional Design. Cognitive Science, 12(2), 257-285. 9 Wang, R., & Chen, L. (2023). AI-Personalized Learning Models: The Future of Student-Centered Education. International Journal of AI in Education, 15(1), 45-67. 10 Sun, J., & Zhao, F. (2023). Preserving Cultural Heritage through AI: Applications in Ancient Literature and Art. Digital Culture & Society, 9(1), 76-98.
Publication Date
6/23/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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