LLMs as Educational Analysts: Transforming Multimodal Data Traces into Actionable Reading Assessment Reports

This paper presents a system that uses LLMs to transform multimodal data (eye-tracking, assessment scores, and teaching standards) into actionable reading assessment reports for K-12 teachers. The system employs unsupervised clustering to identify reading behavior patterns and LLMs to generate teacher-friendly reports that are evaluated by both LLM experts and human educators.

Reading assessments are essential for enhancing students' comprehension, yet many EdTech applications focus mainly on outcome-based metrics, providing limited insights into student behavior and cognition. This study investigates the use of multimodal data sources -- including eye-tracking data, learning outcomes, assessment content, and teaching standards -- to derive meaningful reading insights. We employ unsupervised learning techniques to identify distinct reading behavior patterns, and then

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