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.
Implicit and explicit lan- guage learning: Conditions, processes, and knowledge in SLA and bilingualism35, 47 (2011)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CY 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.