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Source note3 min read

Earthquaker-AI: A Retrieval-Augmented Generation Framework with Rubric-Based Assessment for Primary School Earthquake Education

Source

arXiv

arxiv.org/abs/2607.14046v1

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Source

Plain-Language Brief

The paper introduces Earthquaker-AI, a hybrid educational framework that uses Lego WeDo2 robotics for earthquake simulations and an AI assistant for guided learning and feedback. The system aims to enhance earthquake preparedness among primary school students by aligning their responses with safety guidelines and providing rubric-based verbal feedback.

What the Document Says

  • The paper presents Earthquaker-AI, a hybrid educational framework that integrates conversational AI and Lego WeDo2 robotics for earthquake education in primary schools.
  • The robotics component simulates seismic response, allowing students to interact with sensors and actuators as tangible representations of protective actions.
  • The AI assistant operates as a guided learning mechanism, aligning student responses with safety guidelines, and providing rubric-based verbal feedback for self-regulated learning.
  • Earthquaker-AI follows a progressive learning trajectory aligned with cognitive development, starting from basic recognition of safety actions to verbal production in upper grades.
  • The dialogic module uses Retrieval-Augmented Generation (RAG) to match student queries semantically with official guidelines and generate safe, accurate responses.
  • Experimental evaluation shows high groundedness and accuracy, with a low hallucination rate.

Key Concepts Mentioned

  • Earthquake education
  • AI assistant
  • Retrieval-Augmented Generation
  • Lego WeDo2 robotics
  • Rubric-based assessment

Claims Made by the Document

  • Earthquaker-AI enhances earthquake preparedness and conscious action among primary school students.
  • The system promotes technological literacy, self-regulation, and responsible use of digital systems.

Evidence Used

  • Experimental evaluation shows high groundedness and accuracy with a low hallucination rate.

Methods / Approach

  • Progressive learning trajectory
  • Rubric-based assessment
  • Retrieval-Augmented Generation

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

The system may not be suitable for students outside the primary school age range. The effectiveness of the system may vary depending on the specific context and student's prior knowledge.

Open Questions

  • How effective is Earthquaker-AI in enhancing earthquake preparedness when compared to traditional methods?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • It would be beneficial to compare the effectiveness of Earthquaker-AI with traditional earthquake education methods.

Raw Abstract / Excerpt

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.