生成式人工智能赋能高校辅导员思想政治教育的机制与路径研究——以中央财经大学为例

A Study on the Mechanism and Path of Generative Artificial Intelligence Empowering Ideological and Political Education for College Counselors—A Case Study of Central University of Finance and Economics

摘要: 生成式人工智能的快速发展为高校思想政治教育带来新的技术可能。当前研究多集中于宏观理论探讨，针对辅导员这一具体工作群体的实证研究较少。本研究以中央财经大学为案例，运用问卷调查、深度访谈与准实验研究方法，考察生成式人工智能在辅导员思政工作中的应用现状、影响机制与实践路径。调查发现：中央财经大学辅导员中使用过生成式AI的比例较高(90.5%)，但绝大多数未接受过系统培训(仅12.8%)，培训需求集中在工具使用方法、内容审核与风险把控、学生数据分析方法、AI + 思政教学设计四个方面；学生对现有思政教育形式的满意度为28.6%，对AI生成内容的接受度为83.8%。基于调查结论，研究设计了“智能素材生成–学生画像推送–智能问答”三位一体的工作方案，并配套建设分层培训体系，在信息学院进行为期一学期的准实验。结果显示，实验组辅导员素材准备时间平均缩短57%，学生思政活动参与率提升18.6个百分点。研究明确了生成式人工智能在辅导员工作中的辅助性定位，为财经类高校智慧思政建设提供了参考路径。研究亦发现，AI在提升效率的同时，在“价值唤醒”和“情感共鸣”层面存在天然局限，人机协作的边界仍需在实践中持续探索。

Abstract: The rapid development of generative artificial intelligence has brought new technological possibilities to ideological and political education in colleges and universities. Current research mainly focuses on macro-level theoretical discussions, with relatively few empirical studies specifically targeting counselors. This study takes Central University of Finance and Economics as a case study, using questionnaire surveys, in-depth interviews, and quasi-experimental research methods to examine the current application status, impact mechanisms, and practical paths of generative artificial intelligence in counselors’ ideological and political work. The survey found that a relatively high proportion of counselors at Central University of Finance and Economics have used generative AI (90.5%), but the vast majority have not received systematic training (only 12.8%). Training needs are concentrated in four aspects: tool usage methods, content review and risk control, student data analysis methods, and AI + ideological and political teaching design. Students’ satisfaction with existing forms of ideological and political education is 28.6%, while their acceptance of AI-generated content is 83.8%. Based on the survey findings, this study designed a three-pronged approach: intelligent material generation, student profile recommendation, and intelligent Q&A. A tiered training system was also established, and a one-semester quasi-experiment was conducted in the School of Information Science and Technology. Results showed that the experimental group’s counselors’ material preparation time was reduced by an average of 57%, and student participation in ideological and political activities increased by 18.6 percentage points. The study clarified the auxiliary role of generative artificial intelligence in counselor work and provided a reference path for the construction of smart ideological and political education in finance and economics universities. The study also found that while AI improves efficiency, it has inherent limitations in terms of “value awakening” and “emotional resonance,” and the boundaries of human-machine collaboration still need to be continuously explored in practice.