An Empirical Study on Enhancing Higher-order Competencies in Polymer Physics Through a Teacher-Student-AI Collaborative Model

LIU Qiao-bin; LIU Xu-ran; WANG Xiao-bei

doi:10.14028/j.cnki.1003-3726.2025.25.287

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An Empirical Study on Enhancing Higher-order Competencies in Polymer Physics Through a Teacher-Student-AI Collaborative Model

更新时间：2026-03-21

- An Empirical Study on Enhancing Higher-order Competencies in Polymer Physics Through a Teacher-Student-AI Collaborative Model
- Polymer Bulletin Vol. 39, Issue 4, Pages: 666-674(2026)
- 作者机构：
  
  北华航天工业学院材料工程学院　廊坊 065000
- 作者简介：
- 基金信息：
- DOI：10.14028/j.cnki.1003-3726.2025.25.287
  CLC：
- Received：28 September 2025，
  
  Accepted：10 December 2025，
  
  Online First：12 January 2026，
  
  Published：20 April 2026
- 稿件说明：
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刘巧宾, 刘旭冉, 王晓蓓. “师−生−AI”协同模式提升高分子物理高阶能力的实证研究. 高分子通报, 2026, 39(4), 666–674.

Liu, Q. B.; Liu, X. R.; Wang, X. B. An empirical study on enhancing higher-order competencies in polymer physics through a teacher-student-AI collaborative model. Polym. Bull. (in Chinese), 2026, 39(4), 666–674.
刘巧宾, 刘旭冉, 王晓蓓. “师−生−AI”协同模式提升高分子物理高阶能力的实证研究. 高分子通报, 2026, 39(4), 666–674. DOI： 10.14028/j.cnki.1003-3726.2025.25.287.

Liu, Q. B.; Liu, X. R.; Wang, X. B. An empirical study on enhancing higher-order competencies in polymer physics through a teacher-student-AI collaborative model. Polym. Bull. (in Chinese), 2026, 39(4), 666–674. DOI： 10.14028/j.cnki.1003-3726.2025.25.287.

摘要

针对传统高分子物理教学中存在的“理论抽象、理解困难，实验限制多，高阶能力培养不足”等问题，构建并实施了“师−生−AI”协同育人模式，通过深度整合人工智能技术对教学全流程进行重构。基于“AI辅助知识解构—师生协同探究—创新实践验证”的教学闭环框架，借助可视化工具、虚拟实验平台及AI分析系统，打破了教学的时空限制和认知障碍。在北华航天工业学院2022和2023级功能材料专业开展了为期2学年的教学实践。实验班与对照班的对比结果显示：在高分子物理核心知识点掌握率方面(89.6%对比68.3%)、实验创新方案设计数量(年均12项对比5项)、学科竞赛获奖率(31.2%对比12.5%)等指标上，实验班学生均显著优于对照班；引入AI工具使师生协同效率提升了40%以上。该模式以“高阶性−创新性−挑战度”为核心，所构建的“师−生−AI”协同理论框架，为高分子物理“金课”建设提供了可复制的实践路径，也为智能技术与专业课程的深度融合提供了范式参考。

Abstract

To address the key challenges in traditional Polymer Physics education

such as the difficulty in comprehending abstract theoretical concepts

constraints in experimental training

and insufficient development of higher-order competencies

this study introduces a “teacher-student-AI” collaborative education model that deeply integrates artificial intelligence (AI) technology to redesign the entire teaching process. A closed-loop instructional framework of “AI-assisted knowledge deconstruction-teacher-student collaborative inquiry-innovative practice verification” was established

supported by visualization tools

virtual experiment platforms

and AI-based analytical systems

effectively overcoming the spatiotemporal and cognitive limitations inherent in conventional teaching. Over a two-year period

the model was implemented among students majoring in functional materials (2022 and 2023 cohorts) at the North China Institute of Aerospace Engineering. A comparative analysis between the experimental and control classes revealed that the experimental group demonstrated significantly better performance across multiple metrics: core knowledge point mastery (89.6% versus 68.3%)

annual output of innovative experiment designs (12 versus 5 projects per year)

and award rates in disciplinary competitions (31.2% versus 12.5%). Furthermore

the integration of AI tools enhanced the efficiency of teacher-student collaboration by more than 40%. This model

with “high-order thinking

innovation

and challenge” at its core

establishes a “teacher-student-AI” collaborative theoretical framework. It not only provides a replicable practical path for building a “golden course” in polymer physics but also serves as a paradigmatic reference for the deep integration of intelligent technology and specialized curricula.

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references

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