大学化学

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化学学科推免生考核录取机制的改革与实践

吴玲玲, 黄伟珺, 吴伟泰, 傅钢   

  1. 厦门大学化学化工学院, 福建 厦门 361005
  • 收稿日期:2026-05-20 录用日期:2026-07-24
  • 通讯作者: 傅钢 E-mail:gfu@xmu.edu.cn Gang Fu
  • 基金资助:
    2023–2024年度基础学科拔尖学生培养计划2.0(20232024)；教育部大中小学课程教材研究重大项目(25AC0008)

Reform and Practice of Admission Assessment for Postgraduate Exempt Students in Chemistry

Lingling Wu, Weijun Huang, Weitai Wu, Gang Fu   

  1. College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian Province, China
  • Received:2026-05-20 Accepted:2026-07-24
  • Contact: Gang Fu E-mail:gfu@xmu.edu.cn

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摘要： 生成式人工智能正给推免生考核录取机制带来冲击，导致考生的作答出现同质化现象，学术潜力难以鉴别。根据厦门大学化学化工学院的改革实践，提出由研究构想书写作、AI (artificialintelligence)协同文献汇报考评、群体竞争式面试这三个环节组成的全新考核办法。该方案建立科研流程情境，考查学生未来科研潜力，引导考生善用AI助力科研，同时用真实情境博弈替代标准化作答。实践显示该方案具备区分潜力，但是更大范围样本的验证以及长期培养效果的跟踪事宜仍在不断开展。

关键词: 拔尖人才, 推免生考核录取, 复试考核改革, 生成式人工智能, 科研过程情境

Abstract: Generative artificial intelligence (AI) is disrupting admission assessment mechanisms for postgraduate exempt students, leading to increasingly homogenized applicant responses and making it difficult to identify academic potential. Drawing on the reform practices at the College of Chemistry and Chemical Engineering, Xiamen University, this study proposes a novel assessment framework comprising three components: research proposal writing, AI-assisted literature presentation evaluation, and groupcompetitive interviews. By situating applicants within authentic research process scenarios, the framework shifts the focus from rote reproduction of knowledge to the evaluation of future research potential, encourages applicants to harness AI as a constructive tool in scientific inquiry rather than merely avoiding its misuse, and replaces standardized responses with strategic engagement in realistic contexts. Preliminary implementation indicates that the framework holds promise in differentiating applicant potential; however, validation with larger sample sizes and longitudinal tracking of training outcomes are still ongoing.

Key words: Top-notch talent, Postgraduate exempt admission assessment, Reform of assessment-centered admission, Generative AI, Research-process contexts