华东师范大学学报(教育科学版)

华东师范大学学报(教育科学版) ›› 2026, Vol. 44 ›› Issue (6): 43-55.doi: 10.16382/j.cnki.1000-5560.2026.06.003

• 教育智能化转型 • 上一篇    下一篇

面向课堂教学场景的大模型应用效应评价研究

宋宇1, 周爱民1, 郝昊1, 范梦雅2   

  1. 1. 华东师范大学上海智能教育研究院，上海 200062
    2. 华南师范大学教育科学学院，广州 510631
  • 出版日期:2026-06-01 发布日期:2026-05-27
  • 基金资助:
    国家自然科学基金面上项目“多元情境下面向创新人才培养的课堂教学智能评价研究”（62377014）。

Research on the Effect Evaluation of Large Model Applications in the Field of Classroom Teaching

Yu Song1, Aimin Zhou1, Hao Hao1, Mengya Fan2   

  1. 1. Shanghai Institute of AI for Education, East China Normal University, Shanghai 200062, China
    2. School of Education, South China Normal University, Guangzhou 510631, China
  • Online:2026-06-01 Published:2026-05-27

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摘要：

课堂教学是人才培养的主要渠道，在智能技术迅猛发展的背景下，大规模预训练语言模型已逐步渗透至教育场景，成为驱动教学范式转型的关键技术变量。然而，当前大模型在教育领域的应用效能存在显著异质性，且评估维度多局限于技术性能层面，其教学适切性与目标达成度亟待实证检验。本研究构建了“价值引导-知识建构-思维发展”三维评估模型，采用对比实验设计，对6款主流大模型（含国内外各3款）生成的中小学多学科（语文、数学、外语、理科综合、文科综合）课堂教学文本进行系统评估，并与专家教师设计的教学内容开展对比分析。研究发现：（1）在价值引导维度，教师教学呈现显著的德育主导型特征，国内大模型表现为均衡价值引导模式，在核心价值观各维度表现均衡，而国外大模型则呈现差异化价值导向特征，虽在社会责任等维度表现突出，但在国家认同、文化传承等方面存在结构性缺失；（2）在知识建构维度，教师表现出高度的课程内容聚焦性，而大模型则展现更强的知识延展性，尤其在跨学科知识网络构建方面具有显著优势；（3）在思维发展维度，大模型在促进高阶思维（包括复杂问题解决、知识迁移与创新思维）方面效果显著，而教师教学在陈述性知识掌握和情境化学习体验方面更具优势，但存在思维定势风险。研究旨在为教育实践者科学合理应用大模型、赋能课堂教学提质增效提供参考，为人工智能时代重构“人机协同”的新型教育范式、推动教育高质量发展提供了重要的实证依据。

关键词: 课堂教学, 大模型, 效果评价, 价值引导, 知识建构, 认知共同体.

Abstract:

Classroom teaching serves as the primary channel for talent cultivation. Against the backdrop of rapid intelligent technology development, large-scale pre-trained language models have gradually permeated educational scenarios, becoming a key technological variable driving the transformation of teaching paradigms. However, the current application effectiveness of large models in education exhibits significant heterogeneity, with evaluation dimensions mostly limited to technical performance aspects, while their pedagogical appropriateness and goal attainment urgently require empirical verification. This study constructs a three-dimensional evaluation model of “value guidance-knowledge construction-cognitive development,” adopting a comparative experimental design to systematically evaluate teaching texts generated by six mainstream large models (including three domestic and three international) across multiple K-12 subjects (Chinese, mathematics, foreign languages, integrated sciences, and integrated humanities), and conducts comparative analysis with expert teachers’ parallel lesson teaching records. The findings reveal that, first, in the value guidance dimension, teacher-led teaching demonstrates significant moral education-dominant characteristics, domestic models exhibit balanced value guidance with consistent performance across core value dimensions, while international models show differentiated value orientation—excelling in dimensions like social responsibility but displaying structural deficiencies in national identity and cultural inheritance. Second, in knowledge construction, teachers demonstrate high curriculum content focus, whereas large models exhibit stronger knowledge extensibility, particularly showing significant advantages in constructing interdisciplinary knowledge networks. Finally, in cognitive development, large models prove significantly more effective in promoting higher-order thinking (including complex problem-solving, knowledge transfer, and innovative thinking), while teacher-led teaching excels in declarative knowledge mastery and situated learning experiences but risks cognitive fixation. The study aims to provide references for educational practitioners to scientifically and rationally apply large models to empower classroom teaching improvement, offering important empirical evidence for reconstructing a new “human-machine collaborative” educational paradigm and promoting high-quality educational development in the AI era.

Key words: classroom teaching, large models, effect evaluation, values, knowledge construction, intellectual community