Authors :
Aishwarya G; Surabhi Srinivas; Sanjana T S; Shashank G N; Sidharth K Iyer
Volume/Issue :
Volume 9 - 2024, Issue 11 - November
Google Scholar :
https://tinyurl.com/yt4wrfma
Scribd :
https://tinyurl.com/5cmm5rzv
DOI :
https://doi.org/10.5281/zenodo.14328991
Abstract :
The traditional education model often fails to
address the varied learning styles and personalized needs
of students, leading to disengagement and gaps in
knowledge. "Edu_bot," an AI-powered educational tool, is
designed to overcome these limitations by leveraging
Retrieval-Augmented Generation (RAG) models. Edu_bot
offers personalized learning experiences by summarizing
content, generating quizzes, and providing real-time
support tailored to individual learners' needs. This paper
surveys existing solutions in education technology,
highlights their limitations, and presents Edu_bot as a
solution that integrates dynamic content generation,
personalized feedback, and interactive learning, thus
enhancing student engagement and learning outcomes.
Keywords :
Edu_bot, Personalized learning, Retrieval- Augmented Generation (RAG), Generative AI in education, Adaptive learning,- Educational technology, AI-powered tutoring systems, Real-time feedback, Knowledge retrieval, Interactive learning systems.
References :
- Sahu, N. (2024). The GenAI Revolution: Unleashing the Role of Information Technology in Education. Journal of Educational Technology and Innovation, 15(2), 45-52.
- Wei, X., Wang, Y., Chen, L., & Liu, M. (2023). A Survey on Chain-of-Thought Reasoning in Large Language Models. Artificial Intelligence Review, 30(1), 73-91.
- Huang, Z., & Huang, F. (2023). Survey of Retrieval-Augmented Text Generation in Large Language Models. Journal of AI and Data Science, 27(3), 188-202.
- Soman, S., & Roychowdhury, S. (2024). Observations on Building RAG Systems for Technical Documents. Proceedings of the International Conference on Advanced Computing, 66(4), 233-249.
- Asai, A., Wu, Z., Wang, Y., Sil, A., & Hajishirzi, H. (2023). Self-RAG: Self-reflective Retrieval-Augmented Generation. Journal of Neural Information Processing, 23(5), 115-132.
- Doe, J., & Lee, A. (2023). Rethinking Retrieval-Augmented Language Models for Personalized Document Generation. ACM Transactions on Information Systems, 42(1), Article 16.
- Huang, J., Li, M., & Song, R. (2024). Enhancing Clinical NLP with Large Language Models. Journal of Biomedical Informatics, 85, 321-338.
- Zhang, Y., & Kim, S. (2024). Fact, Fetch, and Reason: A Unified Assessment of Retrieval-Augmented Generation. Journal of Computational Linguistics, 34(3), 401-418.
- Murtaza, T., Rahman, S., & Khan, R. (2024). AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions. International Journal of E-Learning and Education Technology, 22(6), 95-111.
- Becerra, M., Lopez, G., & Chang, T. (2024). Generative AI-Based Personalized Guidance Tool to Improve the Feedback Process Among MOOC Learners. IEEE Transactions on Learning Technologies, 16(2), 222-234.
The traditional education model often fails to
address the varied learning styles and personalized needs
of students, leading to disengagement and gaps in
knowledge. "Edu_bot," an AI-powered educational tool, is
designed to overcome these limitations by leveraging
Retrieval-Augmented Generation (RAG) models. Edu_bot
offers personalized learning experiences by summarizing
content, generating quizzes, and providing real-time
support tailored to individual learners' needs. This paper
surveys existing solutions in education technology,
highlights their limitations, and presents Edu_bot as a
solution that integrates dynamic content generation,
personalized feedback, and interactive learning, thus
enhancing student engagement and learning outcomes.
Keywords :
Edu_bot, Personalized learning, Retrieval- Augmented Generation (RAG), Generative AI in education, Adaptive learning,- Educational technology, AI-powered tutoring systems, Real-time feedback, Knowledge retrieval, Interactive learning systems.