创新创业理论研究与实践 ›› 2026, Vol. 9 ›› Issue (1): 122-124.

• 模式探索 • 上一篇    下一篇

人工智能赋能高校教学评价改革的价值意蕴与实践路径

王宏岳, 尚莹莹   

  1. 深圳技术大学 马克思主义学院/人文社科学院,广东深圳 518118
  • 出版日期:2026-01-10 发布日期:2026-06-30
  • 作者简介:王宏岳(1992—),男,安徽六安人,博士研究生,副教授,研究方向:教育精准化、大历史观。
  • 基金资助:
    广东省普通高校特色创新类项目“基于OBE理念的高校思政课教学目标达成度评价方案研究”(2023WTSCX080);广东省本科高校教学质量与教学改革工程建设项目“精准思政视域下高校‘形势与政策’课‘专题教学+专家讲座+专业实践’混合式教学模式研究”

Value and Practical Path of Empowering University Teaching Evaluation Reform with Artificial Intelligence

WANG Hongyue, SHANG Yingying   

  1. School of Marxism/School of Humanities and Social Sciences, Shenzhen Technology University, Shenzhen Guangdong, 518118, China
  • Online:2026-01-10 Published:2026-06-30

摘要: 近年来,人工智能技术与高校教育教学深度融合,取得了显著成效。在高校教学评价改革中,人工智能以智能技术为依托,以数据为核心驱动,推动教学评价朝着智能化、精准化、科学化方向发展,实现经验性评价向数字性评价、静态型评价向动态型评价、总结式评价向反馈式评价的深刻转型。人工智能赋能教学评价改革,客观上要求教育工作者以建构评价指标为驱动,引导智能系统精准赋能教学评价;以健全反馈机制为渠道,增强评价结果的实践效用;以优化算法设计为动能,完善智能系统的监管机制。

关键词: 人工智能, 教学评价, 价值意蕴, 评价指标, 反馈机制, 算法设计

Abstract: In recent years, artificial intelligence technology has been deeply integrated with higher education and teaching, achieving significant results. In the reform of teaching evaluation in universities, artificial intelligence relies on intelligent technology and is driven by data as the core, promoting the development of teaching evaluation towards intelligence, precision, and scientificity, achieving a profound transformation from empirical evaluation to digital evaluation, static evaluation to dynamic evaluation, and summary evaluation to feedback evaluation. The reform of teaching evaluation empowered by artificial intelligence objectively requires educators to be driven by the construction of evaluation indicators and guide intelligent systems to accurately empower teaching evaluation; using a sound feedback mechanism as a channel to enhance the practical utility of evaluation results; taking optimization algorithm design as the driving force, improve the regulatory mechanism of intelligent systems.

Key words: Artificial intelligence, Teaching evaluation, Value connotation, Evaluation indicators, Feedback mechanism, Algorithm design

中图分类号: