交互式教学内容生成大语言模型的构建与自动化评估

张若华; 刘涛; 卢烨; 宋思宇; 刘文涛; 宋宇; 周爱民; 郝昊

doi:10.16383/j.aas.c260145

交互式教学内容生成大语言模型的构建与自动化评估

doi: 10.16383/j.aas.c260145 cstr: 32138.14.j.aas.c260145

张若华^{1, 2,},
刘涛^{1, 2,},
卢烨^1,,
宋思宇^{1, 2,},
刘文涛^{1, 2, 3,},
宋宇^1,,
周爱民^{1, 2, 3,},
郝昊^1,

1.
华东师范大学上海智能教育研究院上海 200062
2.
华东师范大学计算机科学与技术学院上海 200062
3.
上海创智学院上海 200003

基金项目: 国家自然科学基金(62306174)资助

详细信息

作者简介:
张若华：华东师范大学上海智能教育研究院、计算机科学与技术学院硕士研究生. 主要研究方向为教育大语言模型和模型高效微调. E-mail: 51275901086@stu.ecnu.edu.cn

刘涛：华东师范大学上海智能教育研究院、计算机科学与技术学院硕士研究生. 主要研究方向为教育大语言模型和模型评测. E-mail: 51275901030@stu.ecnu.edu.cn

卢烨：华东师范大学上海智能教育研究院博士研究生. 主要研究方向为教育大语言模型, 强化学习和演化计算. E-mail: 51275901116@stu.ecnu.edu.cn

宋思宇：华东师范大学上海智能教育研究院、计算机科学与技术学院博士研究生. 主要研究方向为教育大语言模型, 强化学习和自进化. E-mail: siyusong00@gmail.com

刘文涛：华东师范大学上海智能教育研究院、计算机科学与技术学院与上海创智学院联合培养博士研究生. 主要研究方向为超长上下文大语言模型和多模态大模型. E-mail: wtliu@stu.ecnu.edu.cn

宋宇：华东师范大学上海智能教育研究院研究员. 主要研究方向为智能课堂教学评价, 人机交互和多智能体协同. E-mail: ysong@mail.ecnu.edu.cn

周爱民：华东师范大学上海智能教育研究院、计算机科学与技术学院教授, 上海创智学院全时导师. 主要研究方向为大语言模型, 智能体系统和演化优化与学习. E-mail: amzhou@cs.ecnu.edu.cn

郝昊：华东师范大学上海智能教育研究院副研究员. 主要研究方向为教育大语言模型, 智能体设计和无梯度优化. 本文通信作者. E-mail: hhao@mail.ecnu.edu.cn

中图分类号: Y
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- 文章访问数: 97
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- 被引次数: 0
出版历程
- 收稿日期: 2026-02-27
- 录用日期: 2026-06-24
- 网络出版日期: 2026-08-18

Construction and Automated Evaluation of Large Language Model for Interactive Teaching Content Generation

Zhang Ruo-Hua^{1, 2
,},
Liu Tao^{1, 2
,},
Lu Ye^1
,,
Song Si-Yu^{1, 2
,},
Liu Wen-Tao^{1, 2, 3
,},
Song Yu^1
,,
Zhou Ai-Min^{1, 2, 3
,},
Hao Hao^1
,

1.
Shanghai Institute of AI for Education, East China Normal University, Shanghai 200062
2.
School of Computer Science and Technology, East China Normal University, Shanghai 200062
3.
Shanghai Innovation Institute, Shanghai 200003

Funds: Supported by National Natural Science Foundation of China (62306174)

More Information

Author Bio:
Zhang Ruo-Hua　Master student at the Shanghai Institute of AI for Education and the School of Computer Science and Technology, East China Normal University. His research interests include educational large language models and efficient fine-tuning

Liu Tao　Master student at the Shanghai Institute of AI for Education and the School of Computer Science and Technology, East China Normal University. His research interests include educational large language models and model evaluation

Lu Ye　Ph.D. candidate at the Shanghai Institute of AI for Education, East China Normal University. His research interests include educational large language models, reinforcement learning, and evolutionary computation

Song Si-Yu　Ph.D. candidate at the Shanghai Institute of AI for Education and the School of Computer Science and Technology, East China Normal University. His research interests include educational large language models, reinforcement learning, and self-evolution

Liu Wen-Tao　Ph.D. candidate jointly trained by the Shanghai Institute of AI for Education and the School of Computer Science and Technology at East China Normal University, and Shanghai Innovation Institute. His research interests include ultra-long-context large language models and multimodal large models

Song Yu　Researcher at the Shanghai Institute of AI for Education, East China Normal University. Her research interests include intelligent classroom teaching evaluation, human-computer interaction, and multi-agent collaboration

Zhou Ai-Min　Professor at the Shanghai Institute of AI for Education and the School of Computer Science and Technology, East China Normal University, and full-time mentor at Shanghai Innovation Institute. His research interests include large language models, agent systems, and evolutionary optimization and learning

Hao Hao　Associate researcher at the Shanghai Institute of AI for Education, East China Normal University. His research interests include educational large language models, agent design, and gradient-free optimization. Corresponding author of this paper

摘要

摘要: 当前教育大语言模型主要依赖文本对话开展知识讲解, 缺乏直观且动态的交互式呈现能力, 从而在复杂概念的可视化与过程性理解支持方面存在一定局限. 为此, 提出一种面向富媒体交互生成的教育大语言模型构建方法及其配套评测框架. 首先, 依据多学科教学大纲并结合细粒度知识点, 构建包含高质量交互式超文本标记语言(HTML)动画以及教学代码的指令数据集, 以缓解高质量数据稀缺的问题. 其次, 构建“监督微调 + 强化学习”的两阶段训练策略: 设计融合代码可执行性与教学相关性的混合奖励模型, 并通过强化学习进一步对齐模型生成内容的教育价值与代码鲁棒性, 从而获得具备交互式HTML教学素材生成能力的领域专用大语言模型. 最后, 针对交互式教学内容缺乏自动化评测体系的现状, 构建涵盖代码正确性、渲染成功率与教学交互性等维度的指标体系, 并实现相应的自动评估管线. 实验结果表明, 所提模型在交互式教学内容生成的准确性与交互性方面优于主流开源与闭源基座模型. 相关模型、数据与评测工具将公开发布, 以支持教育人工智能研究与应用的进一步发展.
- 教育大语言模型 /
- 交互式教学内容生成 /
- HTML 动画 /
- 强化学习 /
- 自动化评测
Abstract: Existing educational large language models (LLMs) primarily rely on text-based dialogue for knowledge explanation, lacking intuitive and dynamic interactive presentation capabilities. Consequently, they face certain limitations in visualizing complex concepts and supporting process-oriented understanding. To address this gap, we propose a method for constructing educational LLMs for rich-media interactive generation, along with a supporting evaluation framework. First, guided by multi-disciplinary curricula and fine-grained knowledge points, we construct an instruction dataset containing high-quality interactive hypertext markup language (HTML) animations and teaching code, alleviating the scarcity of high-quality data. Second, we develop a two-stage training strategy——supervised fine-tuning followed by reinforcement learning (RL). Specifically, we design a hybrid reward model that integrates code executability and pedagogical relevance, and apply RL to further align the educational value of model-generated content with code robustness, resulting in a domain-specific LLM capable of producing interactive HTML teaching materials. Finally, to address the current absence of an automated evaluation system for interactive teaching content, we establish a multi-dimensional metric suite covering code correctness, rendering success rate, and teaching interactivity, and implement an automated evaluation pipeline. Experimental results show that the proposed model outperforms mainstream open-source and closed-source base models in both the accuracy and interactivity of generated interactive teaching content. The related models, datasets, and evaluation tools will be publicly released to support the further development of educational artificial intelligence research and applications.
- educational large language models /
- interactive teaching content generation /
- hypertext markup language animations /
- reinforcement learning /
- automated evaluation

HTML全文

图 1 传统静态资源与生成式交互仿真中的认知加工过程对比

Fig. 1 Comparison of cognitive processing in traditional static resources and generative interactive simulations

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图 2 融合富媒体交互的生成式教育大语言模型总体架构

Fig. 2 Overall architecture of the generative educational large language model with rich-media interaction

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图 3 基于多模态反馈的迭代修正闭环

Fig. 3 Closed-loop iterative refinement based on multimodal feedback

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图 4 级联评估与稀疏视觉采样策略

Fig. 4 Cascaded evaluation and sparse visual sampling strategy

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图 5 多模态奖励模型计算流程图

Fig. 5 Computational flowchart of the multimodal reward model

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图 6 Edu-Eval基准测试集的学科、学段与知识点分布

Fig. 6 Subject, grade-band, and knowledge-point distribution of the Edu-Eval benchmark dataset

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图 7 不同模型的主实验结果

Fig. 7 Main experimental results of different models

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图 8 模型能力多维评估雷达图

Fig. 8 Radar chart of multidimensional model capabilities

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图 9 “牛顿与经典力学”交互式教学内容生成案例

Fig. 9 Case of interactive teaching content generation for Newton and classical mechanics

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表 1 模型配置

Table 1 Model configurations

角色	模型名称	总参数/激活参数	任务
策略模型	Qwen3-30B-A3B-Coder	30.5 B/2.4 B	生成交互式代码
评估模型	Qwen3-VL-235B	235 B/22 B	多模态联合评分

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表 2 不同模型在Edu-Eval测试集上的性能对比

Table 2 Performance comparison of different models on the Edu-Eval test set

模型	总分	语法层(L1)	逻辑层(L2)	视觉层(L3)	交互层(L4)
DeepSeek-V3.2	43.9	34.8	65.0	70.3	46.0
Gemini 3 Pro	48.9	44.0	66.7	59.4	55.0
Qwen3-235B-Instruct	52.8	45.5	71.0	67.9	56.4
Qwen3-30B-A3B-Coder	55.0	46.9	65.3	64.3	64.8
IE-code	63.1	49.6	61.4	74.9	77.7

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表 3 不同训练阶段与数据规模的消融实验结果

Table 3 Ablation study results on different training stages and data scale

模型	总分	视觉层(L3)	交互层(L4)
基座模型	55.0	64.3	64.8
Direct-RL (无SFT)	53.9	68.9	62.2
IE-code-5k-SFT	47.2	57.3	58.7
IE-code-5k-RL	57.6	72.0	70.7
IE-code-10k-SFT	46.3	54.9	58.3
IE-code-10k-RL	63.1	74.9	77.7

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表 4 匿名A/B人类偏好评估结果

Table 4 Anonymous A/B human preference evaluation results

评价结果	样本级结果数	比例
IE-code更好	13	65.0%
Qwen3-30B-A3B-Coder更好	3	15.0%
差不多	2	10.0%
都不合格	1	5.0%
跳过	1	5.0%

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