大学化学

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GenAI赋能分析化学：智能引导式游戏化教学设计

李雁1, 赵高欣2, 陈明星3, 常涛1, 母静波1, 秦身钧1   

  1. 1 河北工程大学材料科学与工程学院, 河北 邯郸 056038;
    2 广州晓安网络科技有限公司, 广东 广州 511400;
    3 北京大学化学与分子工程学院, 北京 100871
  • 收稿日期:2025-11-30 修回日期:2026-03-03
  • 通讯作者: 母静波, 秦身钧 E-mail:jingbomu@hebeu.edu.cn;qinsj528@hebeu.edu.cn Jingbo Mu, Shenjun Qin
  • 基金资助:
    河北省高校人工智能赋能教改专项(“AI+游戏化”双驱赋能分析化学教改：自适应闯关学习系统构建，2025RGZN033)；河北工程大学教育教学改革研究与实践项目(AI赋能教学——“新工科”背景下分析化学课程体系的创新与重构，JG2025019)

Generative AI-enhanced analytical chemistry: intelligent, gamified instructional design

Yan Li1, Gaoxin Zhao2, Mingxing Chen3, Tao Chang1, Jingbo Mu1, Shenjun Qin1   

  1. 1 College of Material Science and Engineering, Hebei University of Engineering, Handan 056038, Hebei Province, China;
    2 Guangzhou Xiao-An Network Technology Co., Ltd., Guangzhou 511400, Guangdong Province, China;
    3 College of Chemistry and Molecular Engineering, Peking University, Beijing 100871
  • Received:2025-11-30 Revised:2026-03-03
  • Contact: Jingbo Mu, Shenjun Qin E-mail:jingbomu@hebeu.edu.cn;qinsj528@hebeu.edu.cn

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摘要： 针对高校分析化学课程普遍存在的课时紧张、学生兴趣不足等痛点，本研究融合自我决定理论、最近发展区及社会建构主义框架，引入生成式人工智能与游戏化教学设计，开发并迭代了五版“分析化学闯关式学习系统”。该系统以情境化知识呈现、游戏化任务机制、智能化反馈引导与多元化激励体系为核心特征，旨在提升学生的内在动机与课堂参与度。初步教学实践表明，该模式能有效激发学生的学习兴趣与主动性，在促进知识理解、提升学习胜任感方面取得积极成效。

关键词: 生成式人工智能, 游戏化教学, 分析化学, 教学改革, 学习兴趣

Abstract: To address common challenges in analytical chemistry courses, such as limited class hours and low student engagement, this study integrates self-determination theory, the zone of proximal development, and social constructivism with generative artificial intelligence (GenAI) and gamification principles. We developed and iteratively refined a five-version “analytical chemistry challenge-based learning system”. This system features contextualized knowledge presentation, gamified task mechanisms, intelligent feedback guidance, and a diversified incentive system, all designed to enhance students’ intrinsic motivation and classroom participation. Preliminary teaching practices demonstrate that this model effectively stimulates students’ learning interest and initiative, showing positive outcomes in promoting knowledge comprehension and fostering a sense of learning competence.

Key words: Generative artificial intelligence, Gamified teaching, Analytical chemistry, Educational reform, Learning interest