Gendo Kumoi, Fumie Watanabe, Tota Suko, Takashi Ishida, et al. (2026) - arXiv preprint (IEEE). arXiv preprint.
Key Findings
- Proposes a human-in-the-loop LLM+TTS pipeline that generates Expert-Novice dialogue lessons, augmenting rather than replacing educators - see ai-generated-content.
- Three-stage workflow (LLM slide/narration generation -> educator review -> automated audiovisual integration) parallels scaffolding design.
- Builds on cognitive apprenticeship theory to structure dialogue narration, supporting active-learning.
- A quasi-experiment with 245 first-year high school students suggests educational potential of dialogue-based lessons.
- Relates to personalized-learning and prior ai-generated-slides-student-perception work on AI-generated instructional material.
- Informs pedagogical-llm-training by showing how LLMs can be steered toward pedagogically sound lesson generation.
Related Pages
- ai-generated-content - LLM-generated lesson material
- ai-generated-slides-student-perception - prior AI-generated instructional content study
- pedagogical-llm-training - steering LLMs for pedagogy
- scaffolding - structured dialogue as scaffold
- personalized-learning - adaptive lesson generation
- active-learning - dialogue-based engagement
- medgame-llm-medical-education-gamification — MedGame: Storytelling Gamification Empowered by Large Langua