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.
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Artificial Intelligence in Education: A Review
2 Pith papers cite this work, alongside 3,469 external citations. Polarity classification is still indexing.
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Experts rate AI scenarios as more likely, less risky, more beneficial, and more valuable than the public, applying different weightings to risk versus benefit.
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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.
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Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI
Experts rate AI scenarios as more likely, less risky, more beneficial, and more valuable than the public, applying different weightings to risk versus benefit.