LLMs as Educational Analysts: Transforming Multimodal Data Traces into Actionable Reading Assessment Reports
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Abstract:
Reading assessments are essential for enhancing students' comprehension; yet, many EdTech applications focus mainly on outcome-based metrics, providing limited insights into students' reading behaviors and cognition. This study investigates the use of multimodal data that includes eye-tracking data, along with learning outcomes, assessment content, and teaching standards to derive meaningful reading insights. We employ unsupervised learning techniques to identify distinct reading behavior patterns. A large language model (LLM) then synthesizes the derived information into actionable reports for educators, streamlining the interpretation process. LLM experts and human educators evaluated 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 educators find beneficial. While automated insight generation shows promise, 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.
Bio: Dr. Eduardo Davalos is an Assistant Professor at Trinity University, working at the intersection of AI in Education (AIED), Human–Computer Interaction (HCI), and Large Language Models (LLMs). His research develops privacy‑preserving, browser‑native sensing and modeling techniques that translate into scalable learning technologies. His PhD is in Computer Science from Vanderbilt University, where he and my team developed RedForest, a e-learning platform that incorporates AI to assist teacher workflows, including assessment creation and other aspects such as gaze analytics and collaborate learning/play. His latest focus is on finding ways to incorporate AI agents to meaningfully assist teachers and students by providing more personalize feedback, suggestions, and content.
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- One Camino Santa Maria
- St. Mary's University of San Antonio
- San Antonio, Texas
- United States 78228
- Building: Blank Sheppard Innovation Center (next to building #22 in the map)
- Room Number: BSIC 203 - Data Science and Machine Learning Lab
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- Starts 06 September 2025 05:00 AM UTC
- Ends 18 November 2025 12:55 AM UTC
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