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Session Type: Paper Session
This session investigates how artificial intelligence and machine learning shape learning design, inquiry, and engagement across educational contexts. The papers span studies of instructional alignment in LLM-generated math tasks, family engagement in online STEM communities, AI mentors guiding scientific reasoning, predictive modeling of metacognitive regulation in game-based learning, and AI-assisted Socratic dialogue in computer science. Collectively, these studies illuminate how AI-mediated environments can foster conceptual understanding, reflective thinking, and authentic collaboration. The session highlights design principles and methodological insights for leveraging AI as an adaptive scaffold that deepens engagement and supports meaningful, data-informed learning experiences.
Evaluating Instructional Alignment in LLM-Generated Math Tasks Using Expert Teacher Annotations - Patrick Oluyori Akinwumi, Clemson University; Meihua Qian, Clemson University
Impact of a GenAI-assisted Socratic Dialogue Tool on Learning in an Asynchronous Computer Science Course - Jeonghyun Lee, Georgia Institute of Technology; Meryem Yilmaz Soylu, Georgia Institute of Technology; Jui-Tse Hung, Georgia Institute of Technology; Diana Popescu, Georgia Institute of Technology; Christopher Cui, University of California - San Diego; Gayane Grigoryan, Georgia Institute of Technology; David Joyner, Georgia Institute of Technology; Stephen W Harmon, Georgia Institute of Technology
Parental Perceptions of Value in a Social Media Space for Early Childhood Family STEM Learning - Valentina Bongiovanni, University of North Florida; Meghan M. Parkinson, University of North Florida; Daniel Dinsmore, University of North Florida
The AI as Cognitive Mentor: Scaffolding Student Scientific Inquiry Through Dialogue - Selcuk Kilinc, Texas A&M University; Tugce Aldemir, Texas A&M University; Vivek Sabanwar, Texas A&M University; Ali Bicer, Texas A&M University; Donggil Song, Texas A&M University
Using Machine Learning Models to Predict Normalized Learning Gains of High-School Students Playing a Simulation Game-Based Learning Environment - Matthew Moreno, University of Central Florida; Megan Wiedbusch, University of Central Florida; Barrie Robinson, University of Idaho; Terrance Soule; Justin Riggs, University of Idaho; Roger Azevedo, University of Central Florida