Optimizing E-Learning Environments: Leveraging Large Language Models for Personalized Education Pathways

DOI

10.2991/978-94-6463-502-7_86How to use a DOI?

Keywords

Personalized Learning; Large Language Models; E-Learning Optimization

Abstract

This study explores the integration of Large Language Models (LLMs) into e-learning platforms to create personalized education pathways, aiming to optimize learning outcomes and user engagement. Recognizing the growing demand for tailored educational experiences, we investigate the potential of LLMs, such as GPT-based models, to dynamically adapt content, assessments, and feedback to individual learner profiles. Our methodology combines quantitative analysis of learner performance data with qualitative feedback from educators and students within a prototype e-learning environment enhanced by LLMs. The key findings suggest that LLM integration significantly improves learning efficiency, increases student satisfaction, and facilitates deeper understanding of complex subjects by providing personalized content and interactive learning experiences. Additionally, our research highlights the importance of ethical considerations and data privacy in deploying AI-driven personalization in education. The implications of our study extend to educational technology developers, policymakers, and educators, underscoring the transformative potential of LLMs in crafting the future of e-learning.

Copyright

© 2024 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  - Fangfang Liu
AU  - Yiyun Wang
AU  - Qiuling Feng
AU  - Linkai Zhu
AU  - Guang Li
PY  - 2024
DA  - 2024/08/31
TI  - Optimizing E-Learning Environments: Leveraging Large Language Models for Personalized Education Pathways
BT  - Proceedings of the 2024 5th International Conference on Education, Knowledge and Information Management (ICEKIM 2024）
PB  - Atlantis Press
SP  - 811
EP  - 817
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-502-7_86
DO  - 10.2991/978-94-6463-502-7_86
ID  - Liu2024
ER  -