Beyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation
arXiv:2602.11790v2 Announce Type: replace-cross Abstract: Although recent end-to-end video generation models demonstrate impressive performance in visually oriented content creation, they remain limited in scenarios that require strict logical rigor and precise knowledge representation, such as instructional and educational media. To address this problem, we propose LASEV, a hierarchical LLM-based multi-agent system for generating high-quality instructional videos from educational problems. LASEV formulates educational video generation as a multi-objective task that simultaneously demands corr
The rapid advancement in large language models and multi-agent system architectures enables more sophisticated approaches to content generation beyond traditional end-to-end models.
This development indicates a move towards more logically rigorous and knowledge-aware AI content creation, particularly for educational and instructional purposes, which could change how information is disseminated and consumed.
The focus shifts from purely visually impressive AI video generation to systems capable of embedding precise knowledge and logical coherence, potentially leading to more reliable and trustworthy AI-generated educational content.
- · EdTech platforms
- · Content creators using AI
- · AI agent developers
- · Educational institutions
- · Traditional educational content producers
- · Basic end-to-end video models
AI-generated educational videos become a scalable and personalized learning resource.
The demand for human educators shifts towards curation, validation, and advanced pedagogical design rather than content creation.
Accessibility to high-quality, personalized education significantly increases globally, narrowing knowledge gaps but potentially raising concerns about algorithmic bias in learning paths.
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Read at arXiv cs.CL