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  1. ; ; ; ; ; ; ; ; ; ; et al (, Data Science for Everyone)
    What do high school students learn from a two-day datathon during which they tackle data to visualize the impact of biased data on healthcare decisions? How do they interact with their team of high school students, data scientists, clinicians, and teachers? What did we, the developers and leaders of the datathon, learn? How would we approach it differently next year? Our goal is to answer these questions plus share lessons learned. We will then divide the audience into teams to brainstorm ways to approach and solve some of the problems we experienced and hopefully recruit some audience members to participate in our June 2025 Brown University Health Artificial Intelligence (AI) Systems Thinking for Equity (HASTE) Datathon in Providence, Rhode Island (Brown University Datathon, 2024). 
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    Free, publicly-accessible full text available February 17, 2026
  2. ; ; ; ; ; ; ; ; (, Learning Letters)
    Student engagement is a key predictor of academic achievement and is closely linked to career awareness, interest, and preparedness. Measuring student engagement during STEM learning is challenging for teachers, given the dynamic and ever-changing nature of these learning environments. Even when engagement data can be collected, leveraging this information to refine and personalize instruction requires significant experience and time. To address this, we are developing Scoutlier EngagEd, a digital teaching assistant that embeds in existing Learning Management Systems (LMS) to automatically and invisibly gather multidimensional data on student engagement and performance during STEM learning. These data are being leveraged to model student learning and generate insights that produce human-like, actionable recommendations through a Large Language Model (LLM) for teachers to improve STEM learning outcomes. 
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    Full Text Available