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The paper presents a dual gatekeeping system for AI-generated educational videos that combines educator input and automated metrics to enhance pedagogical quality, demonstrating that principled resistance to AI outputs improves instructional design.
IKS-Instruct is a multilingual dataset of 24,795 instruction-response pairs for teaching language models Indian Knowledge Systems, spanning seven Indian languages and covering 41 pedagogical techniques. Evaluation shows a fine-tuned 7B model performs competitively with larger general-purpose models on IKS-specific tasks.
LectūraAgents is a multi-agent framework for adaptive personalized learning that mimics professor-student interactions and generates embodied teaching actions aligned with learner profiles. It introduces a hierarchical architecture, an adaptive embodied teaching mechanism, and a Teaching Action-Speech Alignment algorithm, showing consistent improvements over existing approaches.