AI-Powered Smart Study Assistant Using Generative AI
Article Sidebar
Main Article Content
Generative Artificial Intelligence (Gen AI) is revolutionizing the education sector by facilitating personalized self-guided learning with intelligence. In this paper, we propose an AI-based Smart Study Assistant by using Generative AI methods for supporting students in academic works. It uses artificial intelligence for subject-related questions, summarizing lengthy study materials, making notes and assignment help.
It achieves this through the application of natural language processing (NLP) and large language models, which allow the system to understand user queries and generate relevant responses. Also provides concept explanation, question generation, doubt solving in real time, making learning more interactive and efficient.
According to this study, Generative AI can leverage student productivity and maximize their learning curve. This proposed solution provides an example of how AI can transform education by making learning more intelligent and responsive. Future improvements might encompass voice interactivity, multilingual service, and academic database integration.
Downloads
References
Research on Generative AI in Education, 2025
AI-based Learning Systems, 2024
Intelligent Tutoring Systems Review, 2023
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics for study success: Reflections on current empirical findings. British Journal of Educational Technology, 51(5), 1875–1890.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.