Large Language Model-Based Framework for Automatic Handling of Student Queries in Open and Distance Learning (ODL)

DOI

10.2991/978-2-38476-477-8_15How to use a DOI?

Keywords

Large Language Models (LLMs); Open and Distance Learning (ODL); Distance learning; e-Learning; Machine Learning; Artificial Intelligence; Open Distance and Learning (ODL); Student Support Services; Natural Language Processing (NLP) techniques

Abstract

Open and Distance Learning (ODL) is a mode of providing flexible educational opportunities at their doorsteps both in terms of access and multiple modes of acquiring knowledge. In Open and Distance Learning (ODL) institutions it is difficult to offer timely and effective support to students as there is no regular modes of classes, there is no regular contact or communication with the students and there is physical separation between students and teachers. Open and Distance Learning (ODL) systems facing huge challenges of delivering timely and effective student support services over different geographical areas. The Universities working on ODL mode of education is dependent on human and machine interactions since there is no continuous classroom teaching-learning process involved, the application of the Artificial Intelligence fills the gap between the Students and Teachers. With the integration of Artificial Intelligence using Deep Learning and Machine Learning algorithms provide different applications in ODL such as giving continous feedback to the Students, shifting learning platform according to need of the students, automated arrangements of classes, evaluation of the assignments, checking plagiarism, assist students in adopting productive and qualitative learning behaviour. The incorporation of Large Language Models (LLMs) can solve these issues by automating student query responses, thus improving the learning process. The emergence of Large Language Models (LLMs) like GPT-4 has opened up new possibilities for the automation of student query management. This paper suggests an integrated proposed framework that incorporates LLMs into ODL environments to automatically process and respond to student queries. The framework aims to ensure precision, customization and scalability with human-in-the-loop control capabilities for ensuring quality. The present paper formulates a framework for designing an LLM-based system for addressing student questions in ODL. The paper involves a review of the literature, elaborated system design, implementation considerations and conceptual flow diagrams.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kuldeep Sharma
AU  - Anubha Jain
PY  - 2025
DA  - 2025/11/10
TI  - Large Language Model-Based Framework for Automatic Handling of Student Queries in Open and Distance Learning (ODL)
BT  - Proceedings of the 3rd International Conference, Anubhuti: Revitalizing Indian Knowledge Systems for the Modern World (ICIKS 2025)
PB  - Atlantis Press
SP  - 203
EP  - 211
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-477-8_15
DO  - 10.2991/978-2-38476-477-8_15
ID  - Sharma2025
ER  -