从传感器数据到课堂探究：GenAI支持的学校数字孪生数据探索
From Sensor Data to Classroom Inquiry: GenAI-Supported Exploration of School Digital Twin Data
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中文总结 AI 辅助
本文提出一个基于GenAI的聊天机器人，用于查询学校数字孪生数据，经17名教师评估，显示出良好的可用性和教学价值，尤其支持探究式学习。
中文摘要 AI 辅助
教育建筑的数字孪生可以支持以可持续发展为导向的学习，但它们在学校的应用仍然有限。本文介绍了一个基于GenAI的聊天机器人，它构建在希腊两所学校现有数字孪生系统之上，使用来自环境传感器和能源计量表的真实物联网数据。该聊天机器人使教育工作者能够通过自然语言查询实时和历史建筑数据、比较空间，并生成课堂活动创意。该系统在80分钟的研讨会上与17名中学教育工作者进行了评估，他们将其与现有的基于网页的仪表板进行了比较。结果显示，该系统具有较高的感知可用性和教学价值，特别是在探究式学习、假设形成和跨学科课程规划方面。参与者还指出了与响应速度、数据验证、信任以及可视化仪表板的持续价值相关的局限性。总体而言，研究结果表明，GenAI界面可以使数字孪生数据更易于教育使用，前提是它们的设计具有透明度、可验证性和教学基础。
英文摘要
Digital Twins for educational buildings can support sustainability-oriented learning, but their use in schools remains limited. This paper presents a GenAI-based chatbot built on top of an existing Digital Twin for two school buildings in Greece, using real IoT data from environmental sensors and energy meters. The chatbot enables educators to query live and historical building data, compare spaces, and generate ideas for classroom activities through natural language. The system was evaluated in an 80-minute workshop with 17 secondary-school educators, who compared it with an existing web-based dashboard. Results show strong perceived usability and pedagogical value, particularly for inquiry-based learning, hypothesis formation, and interdisciplinary lesson planning. Participants also highlighted limitations related to response speed, data verification, trust, and the continued value of visual dashboards. Overall, the findings suggest that GenAI interfaces can make Digital Twin data more accessible for educational use, provided they are designed with transparency, verification, and pedagogical grounding.
