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AI可视化赋能化工原理重难点模块教学改革与实践——以非均相分离、蒸发、固体干燥为例

刘宝良1, 张其坤2   

  1. 1 齐鲁师范学院化学与化工学院, 山东省抗衰老化妆品工程技术研究中心, 山东 济南 250200;
    2 山东师范大学化学化工与材料科学学院, 山东 济南 250014
  • 收稿日期:2026-07-24 录用日期:2026-09-03
  • 通讯作者: 刘宝良, 张其坤 E-mail:qi9@qlnu.edu.cn;zhangqk@sdnu.edu.cn Baoliang Liu, Qikun Zhang
  • 基金资助:
    山东省自然科学基金(ZR2025QC1920Z, ZR2025MS239)

AI visualization-empowered modular teaching reform and practice for core challenging units of chemical principles: a case study of heterogeneous separation, evaporation, and solid drying

Baoliang Liu1, Qikun Zhang2   

  1. 1 Shandong Provincial Engineering Research Center for Anti-Aging Cosmetics, School of Chemistry and Chemical Engineering, Qilu Normal University, Jinan 250200, Shandong Province, China;
    2 School of Chemistry, Chemical Engineering and Materials Science, Shandong Normal University, Jinan 250014, Shandong Province, China
  • Received:2026-07-24 Accepted:2026-09-03
  • Contact: Baoliang Liu, Qikun Zhang E-mail:qi9@qlnu.edu.cn;zhangqk@sdnu.edu.cn

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摘要： 化工原理是化工类专业衔接基础理论与工程实践的核心课程，其中非均相物系分离、蒸发、固体干燥三大核心章节存在机理抽象、参数耦合复杂、设备选型困难、公式易混淆等教学难题，传统静态板书与图文教学模式难以满足智能化育人与工程能力培养需求。针对以上痛点，本文依托一线课堂教学实践，将AI动态仿真、三维机理图解、参数化模拟、标准化解题模板等可视化技术融入三章模块化教学，构建“机理可视化–知识结构化–分析参数化–资源体系化”的智能化教学路径。教学实践表明，AI可视化教学能够有效破解抽象知识点教学困境，厘清学生知识误区，规范解题思维，显著提升课堂教学效果与学生工艺分析、参数优化的工程素养。通过规范AI使用原则、搭建模块化教学资源库、融入课程思政与多元评价体系，可为同类本科院校化工原理数字化、精准化、模块化教学改革提供可复制、可落地的实践参考。

关键词: AI可视化, 化工原理, 教学改革, 非均相物系分离, 蒸发, 固体干燥

Abstract: Chemical Principles is a core course that bridges fundamental theory and engineering practice for chemical engineering majors. Its three key chapters—heterogeneous system separation, evaporation, and solid drying—pose persistent teaching challenges, including abstract mechanisms, complex parameter coupling, difficult equipment selection, and easily confused formulas. Traditional static blackboard and text-image instruction can hardly meet the demands of intelligent education and engineering competency development. To address these issues, this study draws on frontline classroom teaching practice and integrates AI-based dynamic simulation, three-dimensional mechanism illustration, parametric simulation, and standardized problem-solving templates into modular teaching for these three chapters. It constructs an intelligent teaching pathway characterized by mechanism visualization, knowledge structuring, analysis parameterization, and resource systematization. Teaching practice shows that AI visualization effectively overcomes the difficulties of teaching abstract concepts, clarifies students’ misconceptions, standardizes problem-solving thinking, and significantly improves classroom teaching effectiveness as well as students’ engineering literacy in process analysis and parameter optimization. By regulating AI usage principles, building a modular teaching resource library, and incorporating curriculum ideology and politics along with a diversified evaluation system, this study provides a replicable and implementable practical reference for the digital, precise, and modular teaching reform of chemical principles in similar undergraduate institutions.

Key words: AI visualization, Chemical principles, Teaching reform, Heterogeneous system separation, Evaporation, Solid drying