地震人工智能：一种基于评分标准评估的小学地震教育检索增强生成框架
Earthquaker-AI: A Retrieval-Augmented Generation Framework with Rubric-Based Assessment for Primary School Earthquake Education
AI总结：
研究针对小学生地震教育问题，提出地震人工智能混合教育框架，集成检索增强生成对话式AI助手，结合乐高机器人、评分标准和AI，通过不同年级的渐进学习提升学生地震应对能力，实验显示有高可信度和准确性。
AI中文摘要：
本文介绍了地震人工智能，这是一个混合教育框架，基于先前实施的教育机器人项目，集成了基于检索增强生成的对话式人工智能助手，旨在提高小学生的地震防备意识和自觉行动。该系统扩展了获奖的STEM项目“地震者”，从乐高WeDo2的机械模拟转向认知和元认知处理。机器人组件利用乐高WeDo2自动化模拟地震响应，让学生与传感器和执行器互动。助手作为引导学习机制，将学生回答与安全指南对齐，并提供基于评分标准的口头反馈。它遵循与认知发展一致的渐进学习轨迹，不同年级有不同重点和评估方式。对话模块使用RAG将学生查询与官方指南语义匹配。实验评估显示出高可信度和准确性，低幻觉率。总体而言，地震人工智能结合了实践参与、信息处理和反思性实践，促进了技术素养、自我调节和数字系统的负责任使用，有助于早期危机管理技能。
英文摘要：
This paper presents Earthquaker-AI, a hybrid educational framework building upon a previously implemented educational robotics project by integrating a conversational AI assistant based on Retrieval-Augmented Generation. It aims to enhance earthquake preparedness and conscious action among primary-school students. The system extends the award-winning STEM project Earthquaker moving from mechanical simulation with Lego WeDo2 to cognitive and metacognitive processing. The robotics component uses Lego WeDo2 automation to simulate seismic response, letting students interact with sensors and actuators as tangible representations of protective actions. The assistant operates as a guided learning mechanism aligning student responses with safety guidelines, while providing rubric-based verbal feedback that supports self-regulated learning and calmness under emergency conditions. Earthquaker-AI follows a progressive learning trajectory aligned with cognitive development. In early grades, the focus is on basic recognition of safety actions through multiple-choice questions, assessed via a two-dimensional rubric. In middle grades, students identify correct action sequences through multiple-choice questions, evaluated via a three-axis rubric. In upper grades, the approach shifts to verbal production, requiring short written responses assessed via a four-dimensional rubric that includes clarity of expression. The dialogic module uses RAG to match student queries semantically with official guidelines, generating safe, accurate responses. Experimental evaluation shows high groundedness and accuracy, with a low hallucination rate. Overall, Earthquaker-AI combines hands-on engagement, information processing, and reflective practice. Combining robotics, rubrics, and AI promotes technological literacy, self-regulation, and responsible use of digital systems, contributing to early crisis-management skills.
