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

arXiv: https://arxiv.org/abs/2503.02099 · HTML: https://mlsanity.com/paper/2503.02099/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science](https://mlsanity.com/paper/2403.14565/) (2024)
- [Leveraging LLM Tutoring Systems for Non-Native English Speakers in Introductory CS Courses](https://mlsanity.com/paper/2411.02725/) (2024)
- [Data-driven unsupervised clustering of online learner behaviour](https://mlsanity.com/paper/1902.04047/) (2019)
- [Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics Cycle](https://mlsanity.com/paper/2312.00087/) (2023)
- [A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments](https://mlsanity.com/paper/2405.06203/) (2024)
- [Multimodal Methods for Analyzing Learning and Training Environments: A Systematic Literature Review](https://mlsanity.com/paper/2408.14491/) (2024)
- [Beyond Instructed Tasks: Recognizing In-the-Wild Reading Behaviors in the Classroom Using Eye Tracking](https://mlsanity.com/paper/2501.18468/) (2025)

## Built on by (most cited first, 3 of 3)

- [AI Misuse in Education Is a Measurement Problem: Toward a Learning Visibility Framework](https://mlsanity.com/paper/2603.07834/) (2026)
- [Designing Gaze Analytics for ELA Instruction: A User-Centered Dashboard with Conversational AI Support](https://mlsanity.com/paper/2509.03741/) (2025)
- [Can MLLMs Read Students' Minds? Unpacking Multimodal Error Analysis in Handwritten Math](https://mlsanity.com/paper/2603.24961/) (2026)

## Abstract (arXiv)

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 a large language model (LLM) synthesizes the derived information into actionable reports for educators, streamlining the interpretation process. LLM experts and human educators evaluate these reports for clarity, accuracy, relevance, and pedagogical usefulness. Our findings indicate that LLMs can effectively function as educational analysts, turning diverse data into teacher-friendly insights that are well-received by educators. While promising for automating insight generation, human oversight remains crucial to ensure reliability and fairness. This research advances human-centered AI in education, connecting data-driven analytics with practical classroom applications.