Research on the Application of Large Language Models in Accounting Course Teaching
- DOI
- 10.2991/978-2-38476-487-7_16How to use a DOI?
- Keywords
- large language models (LLMs); accounting teaching; prompt; application scenario
- Abstract
Against the backdrop of the rapid development of large language models (LLMs) in the field of education, traditional accounting teaching faces numerous challenges, while the application of LLMs presents new opportunities for innovation in accounting teaching. Starting from the core functionalities of LLMs and the current state of accounting education, this paper analyzes the feasibility of applying LLMs to accounting teaching. It delves into exploring the application scenarios of LLMs in accounting course teaching, including assisting teachers in lesson preparation, facilitating personalized student learning, explaining accounting concepts and addressing queries, and generating accounting exercises. Furthermore, it introduces a step-by-step framework to guide students in formulating precise prompts. Simultaneously, it addresses corresponding measures for challenges encountered in application, such as data privacy and security, inaccurate information, and over-reliance.
- 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.
Cite this article
TY - CONF AU - Shuang Ke PY - 2025 DA - 2025/11/10 TI - Research on the Application of Large Language Models in Accounting Course Teaching BT - Proceedings of the 2025 International Conference on Digital Technology and Educational Psychology (DTEP 2025) PB - Atlantis Press SP - 175 EP - 186 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-487-7_16 DO - 10.2991/978-2-38476-487-7_16 ID - Ke2025 ER -