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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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
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Kinetiq integrates natural gestures into data problem-solving, yielding higher enjoyment, engagement, and motivation with learning gains comparable to conventional click-based platforms.
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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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From Clicking to Moving: Embodied Micro-Movements as a New Modality for Data Literacy Learning
Kinetiq integrates natural gestures into data problem-solving, yielding higher enjoyment, engagement, and motivation with learning gains comparable to conventional click-based platforms.