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生成式人工智能辅助钙钛矿晶体结构教学资源建设与应用

张乐1,2, 李敏1,2, 王琳艳1, 刘清泉1,2   

  1. 1. 湖南科技大学材料科学与工程学院，湖南 湘潭 411201;
    2. 湖南省先进材料现代产业学院，湖南 湘潭 411201
  • 收稿日期:2026-04-30 录用日期:2026-07-31
  • 通讯作者: 刘清泉 E-mail:qqliu@hnust.edu.cn Qingquan Liu
  • 基金资助:
    2022年湖南省普通高等学校教学改革研究重点项目(HNJG-2022-0175, 202502000796)；湖南省新材料现代产业学院(湘教通［2023］379号)；2026年湖南省新工科项目：工科教师AI教学能力提升的智慧教学新范式构建与实践(湘教通［2026］74号)

Construction and application of teaching resources for perovskite crystal structure assisted by generative artificial intelligence

Le Zhang1,2, Min Li1,2, Linyan Wang1, Qingquan Liu1,2   

  1. 1. School of Materials Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan Province, China;
    2. Hunan Provincial Modern Industry College of Advanced Materials, Xiangtan 411201, Hunan Province, China
  • Received:2026-04-30 Accepted:2026-07-31
  • Contact: Qingquan Liu E-mail:qqliu@hnust.edu.cn

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摘要： 针对晶体结构教学中三维空间理解困难以及传统三维建模软件课堂应用门槛较高的问题，本研究利用生成式人工智能(Generative Artificial Intelligence，以下简称GenAI)构建晶胞位点图、配位多面体图和交互式教学资源，并在本科材料类专业“材料科学基础”课程中开展课堂实践。结果表明，该资源有助于学生理解A/B/X位点、BX6八面体及角共享连接方式，提升不同结构表征之间的转换能力。学生反馈显示，可视化资源提高了课堂直观性，但同时需要加强人工智能生成内容(Artificial Intelligence Generated Content，AIGC)的核验教学。研究表明，GenAI可作为晶体结构教学的有效辅助工具，并为材料科学及相关理工课程的教学资源建设提供参考。

关键词: 生成式人工智能, 钙钛矿, 晶体结构, 可视化教学, 教学资源建设

Abstract: Addressing the challenges of comprehending three-dimensional spatial arrangements in crystal structure instruction and the high barriers to classroom use of conventional 3D modeling software, this study employed generative artificial intelligence (GenAI) to develop unit-cell site diagrams, coordination polyhedron illustrations, and interactive teaching resources. These materials were implemented in classroom practice within the undergraduate course “Fundamentals of Materials Science” for materials science majors. The results demonstrated that these resources facilitated students’ understanding of A/B/X sites, BX6 octahedra, and corner-sharing connectivity, while also enhancing their ability to convert between different structural representations. Student feedback indicated that the visualized resources improved classroom intuitiveness, while also underscoring the need for reinforced instruction on verifying AI-generated content. The findings suggest that AIGC can serve as an effective auxiliary tool for crystal structure instruction and offer a valuable reference for developing teaching resources in materials science and related science and engineering disciplines.

Key words: Generative artificial intelligence, Perovskite, Crystal structure, Visualization-based teaching, Teaching resource development