LectūraAgents proposes a hierarchical multi-agent system with adaptive embodied teaching and the TASA algorithm for personalized AI-assisted learning, reporting gains in content quality, teaching actions, and personalization over baselines via expert educator validation on sample courses.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Lect\=uraAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching
LectūraAgents proposes a hierarchical multi-agent system with adaptive embodied teaching and the TASA algorithm for personalized AI-assisted learning, reporting gains in content quality, teaching actions, and personalization over baselines via expert educator validation on sample courses.