大学化学

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数字赋能下大学化学“结论前置”教学的优化路径探索——基于理解导向的教学重构

何学侠1, 胡鹏2, 陈沛1, 刘宗怀1, 刘彬1, 赵奎1   

  1. 1 陕西师范大学材料科学与工程学院, 陕西 西安 710119;
    2 西北大学物理学院, 陕西 西安 710127
  • 收稿日期:2026-01-22 录用日期:2026-04-02
  • 通讯作者: 何学侠, 赵奎 E-mail:xxhe@snnu.edu.cn;zhaok@snnu.edu.cn Xuexia He, Kui Zhao
  • 基金资助:
    陕西师范大学校级本科教育教学改革“揭榜挂帅”项目-新工科背景下针对材料化学人才培养效能提升的无机化学理论-实践-研讨三位一体教学改革研究(24JBGS31)

Exploration of optimization paths for “conclusion-first” teaching in university chemistry under digital empowerment: teaching reconstruction based on comprehension orientation

Xuexia He1, Peng Hu2, Pei Chen1, Zong-Huai Liu1, Bin Liu1, Kui Zhao1   

  1. 1 School of Materials Science and Engineering, Shaanxi Normal University, Xi'an 710119, Shaanxi Province, China;
    2 School of Physics, Northwestern University, Xi'an 710127, Shaanxi Province, China
  • Received:2026-01-22 Accepted:2026-04-02
  • Contact: Xuexia He, Kui Zhao E-mail:xxhe@snnu.edu.cn;zhaok@snnu.edu.cn

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摘要： 大学化学基础课程(无机化学、物理化学等)中，我们把“直接呈现核心结论、省略复杂推导过程”的教学安排称为“结论前置”教学模式。该模式是学时限制与学科知识层级制约下的现实选择，但易导致学生“知其然而不知其所以然”，阻碍知识深度理解与科学思维培养。在人工智能与数字教育深度融合的背景下，本文聚焦“如何通过数字赋能让学生理解前置结论的形成逻辑、夯实多课程知识体系”，系统分析在大学化学基础课中“结论前置”的共性成因与认知痛点，构建“理论铺垫-可视化阐释-互动探究-体系联通”四位一体的优化路径。通过数字辅助的虚拟仿真、动态逻辑推演、跨课程互动探究等数字化策略，还原“结论前置”省略的推导逻辑与理论背景，帮助学生从“被动记忆结论”转向“主动建构知识”。文中以大学化学“原子结构”和“化学热力学”教学为例，验证该路径在多课程中的适配性与成效，为人工智能时代大学化学基础课教学改革提供可推广的参考方案。

关键词: 数字赋能, 大学化学, 结论前置, 教学优化, 可视化教学, 批判性思维

Abstract: In fundamental chemistry courses at the university level (e.g., Inorganic Chemistry, Physical Chemistry), the “conclusion-first” teaching model is characterized by presenting core conclusions directly while omitting complex derivation processes. This approach often arises as a practical compromise due to constraints such as limited class hours and the hierarchical nature of disciplinary knowledge. However, it tends to result in superficial learning, where students grasp “what” but not “why”, thereby impeding deep comprehension and the development of scientific thinking. Against the backdrop of deepening integration of artificial intelligence (AI) and digital education, this study focuses on how digital empowerment can help students understand the logical foundations of pre-presented conclusions and reinforce interdisciplinary knowledge systems. It systematically examines the root causes and cognitive challenges associated with the “conclusion-first” model in university fundamental chemistry courses and proposes a four-pronged optimization framework: Conclusion Tracing, Logic Visualization, Inquiry Verification, and System Anchoring. By leveraging digital strategies—such as virtual simulations, dynamic logic deduction, and cross-course interactive inquiry—this approach restores the omitted deductive logic and theoretical context inherent in the “conclusion-first” model. Consequently, it facilitates a shift from passive memorization of conclusions to active knowledge construction. Using the teaching of “Atomic Structure” and “Chemical Thermodynamics” (Physical Chemistry) as case studies, this research demonstrates the adaptability and efficacy of the proposed framework across multiple courses, offering a transferable model for reforming foundational chemistry education in the AI era.

Key words: Digital empowerment, University chemistry, Conclusion-first, Teaching optimization, Visual teaching, Critical thinking