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Implementation of a Smart Lecture Video Generation System Using Open Source AI

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2025, 30(12), pp.73~85
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 29, 2025
  • Accepted : November 24, 2025
  • Published : December 31, 2025

SeongHun Kim 1 Oh-Gyu Kwon 2 Seong-Guk Nam 3 Seung-Cheol Lee 1 Tae-Young Yang 1 Jae-Woo Ryu 3 Chang-Hyeon Park 1

1영남대학교
2모비핀테크놀러지
3니어네트웍스

Accredited

ABSTRACT

In this study, we propose an open-source AI-driven lecture video generation system that automatically produces instructional videos from university lecture materials. The proposed system integrates large language models (LLMs) and retrieval-augmented generation (RAG) to generate lecture scripts, synthesize speech through text-to-speech (TTS), and create lip-synced digital human videos in a unified pipeline. To construct a domain-specific knowledge base, lecture materials were collected from two Korean universities. Using the LangChain framework, the system connects HyperCLOVA X SEED 3B with a RAG module to generate lecture scripts tailored to an instructor’s intent. The generated scripts are then processed by GPT-SoVITS for TTS synthesis and by a digital-human rendering module to produce realistic lecture videos. Experimental results show that integrating RAG improves performance over an LLM-only baseline by 5.6% in BERTScore, 3.3% in ROUGE-L, and 3.6% in BLEU. These improvements indicate that the proposed RAG structure enhances factual accuracy and logical consistency by referencing definitional and exemplar statements from external knowledge bases. This work demonstrates that even without fine-tuning, a RAG-based approach can be effectively applied to educational LLMs. It also provides foundational technology for future AI-driven smart-education platforms.

Citation status

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