Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 34, 29 April 2024


Open Access | Article

Application of large language models in the field of education

Shiyi Shen * 1
1 University of Pennsylvania

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 34, 140-147
Published 29 April 2024. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Shiyi Shen. Application of large language models in the field of education. TNS (2024) Vol. 34: 140-147. DOI: 10.54254/2753-8818/34/20241163.

Abstract

This paper delves into the application of generative artificial intelligence and large language models in the field of education, with a particular focus on the rising trend of large language models. Large language models play a crucial role in intelligent tutoring systems by accurately understanding student queries through deep learning and providing personalized responses. Case studies showcase the exemplary utilization of natural language processing techniques and reasoning engines, albeit facing challenges related to real-time processing and privacy concerns. The latter part of the paper concentrates on the application of generative artificial intelligence and large language models in curiosity-driven learning and the integration of multimodal educational systems, emphasizing the technical frameworks and challenges associated with multimodal integration. Finally, the paper provides insights into future developments, highlighting research on the potential benefits in the field of education, while emphasizing concerns related to ethics and privacy.

Keywords

Generative Artificial Intelligence, Large Language Models, Educational Technology, Intelligent Tutoring Systems, Curiosity-Driven Learning, Problem Solving

References

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7. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., . . . Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
ISBN (Print)
978-1-83558-369-2
ISBN (Online)
978-1-83558-370-8
Published Date
29 April 2024
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/34/20241163
Copyright
29 April 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated