大学化学

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“图谱导航+AI诊断”双轮驱动教学实践——以能斯特方程为例

赵博武, 俞强, 薄涛   

  1. 华北理工大学化学工程学院, 河北 唐山 063210
  • 收稿日期:2026-04-30 修回日期:2026-07-03
  • 通讯作者: 薄涛 E-mail:botao@ncst.edu.cn Tao Bo
  • 基金资助:
    华北理工大学校级教育教学改革研究与实践项目(L2340,ZJ2209,ZZJ2402Z)，河北省高等教育教学改革研究与实践项目(2026GJJG216)

“Knowledge graph + AI diagnosis” dual-driven teaching practice: a case of the Nernst equation

Bowu Zhao, Qiang Yu, Tao Bo   

  1. College of Chemical Engineering, North China University of Science and Technology, Tangshan 063210, Hebei Province, China
  • Received:2026-04-30 Revised:2026-07-03
  • Contact: Tao Bo E-mail:botao@ncst.edu.cn

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摘要： 无机化学是高校化工、化学、材料等专业学生必修的四大化学之一，但在教学过程中发现学生难以建立前后知识之间的有效关联，致使知识碎片化，无法利用先学知识指导后期学习。研究以能斯特方程教学为例，利用知识图谱构建了“基础知识-核心推导-综合应用”三层知识网络，并利用AI搭建了智能答疑系统。同时，在教学过程中辅以智能学情画像。实践后发现，该方法不仅提升了学生知识的连贯性和应用能力，还实现了教学干预从经验直觉向数据驱动的转变。

关键词: 知识图谱, AI诊断, AI赋能, 无机化学, 能斯特方程

Abstract: Inorganic Chemistry is one of the four core chemistry courses required for students majoring in chemical engineering, chemistry, materials science, and related fields in higher education. However, it has been observed in teaching practice that students often struggle to establish effective connections between prior and subsequent knowledge, leading to fragmented learning and an inability to apply previous ly acquired knowledge to guide later studies. Taking the teaching of the Nernst equation as an example, this study constructed a three-tier knowledge network—“basic knowledge, core derivation, and comprehensive application”—using a knowledge graph, and developed an AI-powered intelligent question-answering system. Additionally, intelligent learning profiling was integrated into the teaching process. Practice demonstrated that this approach not only improved students’ knowledge coherence and application skills but also shifted teaching interventions from experience-based intuition to a data-driven paradigm.

Key words: Knowledge graph, AI diagnosis, AI empowerment, Inorganic chemistry, Nernst equation