# From Content Generation to Learning Support: Pedagogy-Guided Generative Video Tutors for STEM Learning

arXiv: https://arxiv.org/abs/2609.24083 · HTML: https://mlsanity.com/paper/2609.24083/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [Simulating Classroom Education with LLM-Empowered Agents](https://mlsanity.com/paper/2406.19226/) (2024)
- [AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails](https://mlsanity.com/paper/2402.09216/) (2024)
- [Code2Video: A Code-centric Paradigm for Educational Video Generation](https://mlsanity.com/paper/2510.01174/) (2025)
- [Ruffle&Riley: Insights from Designing and Evaluating a Large Language Model-Based Conversational Tutoring System](https://mlsanity.com/paper/2404.17460/) (2024)
- [TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understanding](https://mlsanity.com/paper/2502.19400/) (2025)
- [EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework](https://mlsanity.com/paper/2504.14928/) (2025)
- [Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study](https://mlsanity.com/paper/2504.05570/) (2025)
- [GAIDE: A Framework for Using Generative AI to Assist in Course Content Development](https://mlsanity.com/paper/2308.12276/) (2023)
- [A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents](https://mlsanity.com/paper/2508.01503/) (2025)
- [Position: LLMs Can be Good Tutors in English Education](https://mlsanity.com/paper/2502.05467/) (2025)
- [Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfaces](https://mlsanity.com/paper/2405.14713/) (2024)
- [Generative AI in Education: From Foundational Insights to the Socratic Playground for Learning](https://mlsanity.com/paper/2501.06682/) (2025)
- [PedaCo-Gen: Scaffolding Pedagogical Agency in Human-AI Collaborative Video Authoring](https://mlsanity.com/paper/2602.19623/) (2026)
- [EduIllustrate: Towards Scalable Automated Generation Of Multimodal Educational Content](https://mlsanity.com/paper/2604.05005/) (2026)

## Abstract (arXiv)

Generative AI enables scalable production of educational videos, but current systems largely focus on producing visually coherent content rather than supporting learning. As a result, generated videos often lack explicit pedagogical structure, reliable quality control, and mechanisms for assessing learner understanding or addressing misconceptions. In this work, we introduce PIVOT (Pedagogy-guided Instructional VideO Tutoring), a generative video tutoring framework for STEM learning via learning-centered instructional support.1 Inspired by conventional teaching workflows, our framework integrates pedagogy into the full generation pipeline: it first uses instructional principles to guide storyboard generation, then produces verified multimodal videos through code-centric generation and a pedagogical verification harness, and finally connects videos with assessment and misconception-aware remediation. Experiments and expert evaluations across four STEM domains show that our framework produces educational videos with pedagogically aligned content, clear and engaging presentation, coherent instructional flow, and perceived effectiveness for learning. These findings suggest a human-centered perspective on educational content generation: generative systems should be evaluated and designed not only by what they produce, but also by how they support teaching practices, learner understanding, and corrective feedback.