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人工智能赋能化学学术型研究生培养模式的研究与探索

罗云杰, 谢洪珍   

  1. 宁波大学材料科学与化学工程学院，浙江 宁波 315211
  • 收稿日期:2025-12-26 录用日期:2026-01-19
  • 通讯作者: 谢洪珍 E-mail:xiehongzhen@nbu.edu.cn Hongzhen Xie
  • 基金资助:
    浙江省“十四五”第二批本科省级教学改革重点项目(JGZD2024017)；浙江省“十四五”第二批研究生省级教学改革工程教育专项(JGGC2024011)；浙江省研究生教育省级教学改革项目(JGCG2025580)；浙江省普通本科高校“十四五”重点教材建设项目(分析化学实验)；宁波大学人工智能课程(分析化学)

Research and exploration on AI-empowered training models for chemistry graduate students

Yunjie Luo, Hongzhen Xie   

  1. School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, Zhejiang Province, China
  • Received:2025-12-26 Accepted:2026-01-19
  • Contact: Hongzhen Xie E-mail:xiehongzhen@nbu.edu.cn

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摘要： 人工智能(AI)技术的迅猛发展，为研究生培养带来了全新的机遇与挑战，同时也凸显出传统培养模式中存在的问题。本文针对化学学术型研究生培养中存在的AI应用与学科脱节、评价体系滞后及学术诚信风险等问题，构建了“四位一体”的创新培养模式。该模式以课程体系为基础，推动AI与专业课程深度融合；以科研流程为核心，优化全链条“人机协同”；以评价机制为保障，建立过程与能力导向的多元评价；以技术环境为支撑，强化赋能与规范。本研究旨在为化学及相关学科学术型研究生培养提供一套系统性改革方案，促进研究生创新能力与学术素养的协同发展，对深化“人工智能+高等教育”实践具有参考价值。

关键词: 人工智能赋能, 培养模式, 化学学术型研究生, 多元评价

Abstract: The rapid development of artificial intelligence (AI) technology presents both unprecedented opportunities and challenges for graduate education, while simultaneously revealing limitations in traditional training approaches. This study addresses three critical issues in chemistry graduate education: the disconnect between AI applications and disciplinary knowledge, outdated evaluation systems, and academic integrity risks. We propose an innovative “four-in-one” training model comprising: (1) an integrated curriculum system that deeply embeds AI in specialized courses; (2) an optimized research workflow emphasizing human-machine collaboration throughout the research process; (3) a competency-oriented, multi-dimensional evaluation mechanism; and (4) a supportive technical environment that balances capability enhancement with ethical regulation. This systematic reform framework aims to foster the coordinated development of research innovation capacity and academic literacy among chemistry graduate students, providing valuable insights for implementing “AI + Higher Education” initiatives in STEM disciplines.

Key words: AI empowerment, Training mode, Academic graduate student in chemistry, Multidimensional evaluation