Application Rules and Optimization Path of Generative AI in Literature Review: Empirical Evidence Based on 104 Public Administration Students’ Assignments

DOI

10.2991/978-94-6239-766-8_25How to use a DOI?

Keywords

AI; literature review; public administration; academic writing; human-machine collaboration

Abstract

This study adopts a qualitative research method, taking the AI usage records of 104 public administration students who completed literature review assignments as research data. It systematically analyzes students’ application logic, operational steps and content distribution rules of generative AI, sorts out differentiated manual revision behaviors between undergraduates and postgraduates, and quantifies the proportion difference of AI-generated content in different writing modules. On this basis, the paper clarifies the practical value and functional boundaries of AI in academic writing, summarizes prominent hidden risks in current human-machine collaborative writing, and puts forward targeted classroom teaching optimization strategies. The research aims to provide empirical support for colleges to formulate standardized AI usage specifications and improve the independent academic writing ability of public administration majors.

Copyright

© 2026 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  - Mengjiao Zhang
AU  - Wenyi Lin
PY  - 2026
DA  - 2026/09/04
TI  - Application Rules and Optimization Path of Generative AI in Literature Review: Empirical Evidence Based on 104 Public Administration Students’ Assignments
BT  - Proceedings of the 2026 6th International Conference on Education, Information Management and Service Science  (EIMSS 2026)
PB  - Atlantis Press
SP  - 242
EP  - 249
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6239-766-8_25
DO  - 10.2991/978-94-6239-766-8_25
ID  - Zhang2026
ER  -