A Study on Quality Improvement of Undergraduate Theses in the Generative Artificial Intelligence Era

DOI

10.2991/978-2-38476-479-2_20How to use a DOI?

Keywords

Generative Artificial Intelligence; Thesis; Undergraduate; Scholarly Research

Abstract

With the rapid development of science and technology, generative artificial intelligence has experienced significant growth in recent years. With its powerful data integration capabilities and text generation function, it has become a valuable assistant for undergraduate students writing their theses. In practical applications, it can quickly refine key information from a massive amount of literature and provide undergraduates with real-time thesis writing advice in a personalized, one-to-one interaction mode, significantly improving thesis writing efficiency. However, there are always two sides to the coin, and the widespread application of generative artificial intelligence has also brought many problems. Some undergraduates rely excessively on AI, and even directly put together their generated content into a thesis, resulting in frequent academic misconduct, and the quality of the thesis not only did not improve but also declined significantly. The current undergraduate thesis is generally characterized by problems such as irrational topic selection, lack of scientificity in research, and inaccurate textual expressions. In order to solve these problems, it is proposed to actively explore the strategy of positively empowering undergraduate thesis quality improvement by AI in the era of generative artificial intelligence by clarifying the boundaries of AI use, shaping the new academic ecology of human-computer interaction, and integrating into the research mentorship system.

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  - Shengji Xia
AU  - Changqing Wang
PY  - 2025
DA  - 2025/11/19
TI  - A Study on Quality Improvement of Undergraduate Theses in the Generative Artificial Intelligence Era
BT  - Proceedings of the 2025 International Conference on Education Research and Training Technologies (ERTT 2025)
PB  - Atlantis Press
SP  - 169
EP  - 177
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-479-2_20
DO  - 10.2991/978-2-38476-479-2_20
ID  - Xia2025
ER  -