Learning in Blocks uses heterogeneous multi-agent debate to score CEFR-aligned conversational competence, enforce 70% mastery progression, and deliver spaced review, yielding better outcomes than feedback alone in an 8-week study of 180 A2 learners.
Journal of Educational Data Mining17(1), 308–336 (2025)
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Learning in Blocks: A Multi Agent Debate Assisted Personalized Adaptive Learning Framework for Language Learning
Learning in Blocks uses heterogeneous multi-agent debate to score CEFR-aligned conversational competence, enforce 70% mastery progression, and deliver spaced review, yielding better outcomes than feedback alone in an 8-week study of 180 A2 learners.