A paper session on collaborative problem-solving, modeling how dialogue, metacognitive regulation, and knowledge building unfold during group work in math and other domains. Studies use methods such as ordered network analysis and Hidden Markov Models, and examine collaboration-competition dynamics and the emergence of flexible strategies.
Long papers on epistemic cognition examine how learners coordinate knowledge and evaluate sources, including physics students' math-concept integration and high schoolers' epistemic vigilance toward everyday science. Additional talks cover epistemic navigation in a unit on Traditional Chinese Medicine and how laypeople resolve expert disagreement.
Long papers focus on pre-service teachers and dialogic conversation, including science teachers rehearsing questioning with AI-based and VR classroom simulators. Other talks examine an AI-feedback co-design intervention for math tutors' productive talk and learning-analytics-supported reflective assessment for knowledge building.
Four long papers use multimodal data to study how emotions and teacher support are regulated during collaborative learning. Topics range from emotion regulation in student dyads building computational models to teacher orchestration load, proximity effects, and scaffolding via classroom chatbots.
Four quantitative papers linking motivation, identity, belonging, and achievement, several at large scale. Topics span equity-oriented pedagogy predicting college success, the attenuation of math identity across the high-school-to-STEM transition, motivation mediating belonging and STEM persistence, and affective-cognitive links in primary students' scientific literacy.
Four papers examining how authority and agency are distributed among students, teachers, and AI tools during learning. Threads include authority in elementary science modeling, how students remix AI chatbots into dialogic tools, epistemic agency in student-LLM math problem solving, and analytics-supported reflective assessment for pre-service teachers.
Four papers on how programming environments and AI are reshaping computer science education. Topics include balancing usability and expressiveness in block-based modeling, Parsons puzzles paired with self-explanation, teachers' shifting conceptions of AI/ML and algorithmic justice, and norms around when copying code is acceptable.