# PedaCo-Gen: Scaffolding Pedagogical Agency in Human-AI Collaborative Video Authoring

arXiv: https://arxiv.org/abs/2602.19623 · HTML: https://mlsanity.com/paper/2602.19623/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 23 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Respect the model: Fine-grained and Robust Explanation with Sharing Ratio Decomposition](https://mlsanity.com/paper/2402.03348/) (2024)
- [Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfaces](https://mlsanity.com/paper/2405.14713/) (2024)

## Built on by (most cited first, 1 of 1)

- [From Content Generation to Learning Support: Pedagogy-Guided Generative Video Tutors for STEM Learning](https://mlsanity.com/paper/2609.24083/) (2026)

## Abstract (arXiv)

While advancements in Text-to-Video (T2V) generative AI offer a promising path toward democratizing content creation, current models are often optimized for visual fidelity rather than instructional efficacy. This study introduces PedaCo-Gen, a pedagogically-informed human-AI collaborative video generating system for authoring instructional videos based on Mayer's Cognitive Theory of Multimedia Learning (CTML). Moving away from traditional "one-shot" generation, PedaCo-Gen introduces an Intermediate Representation (IR) phase, enabling educators to interactively review and refine video blueprints-comprising scripts and visual descriptions-with an AI reviewer. Our study with 23 education experts demonstrates that PedaCo-Gen significantly enhances video quality across various topics and CTML principles compared to baselines. Participants perceived the AI-driven guidance not merely as a set of instructions but as a metacognitive scaffold that augmented their instructional design expertise, reporting high production efficiency (M=4.26) and guide validity (M=4.04). These findings highlight the importance of reclaiming pedagogical agency through principled co-creation, providing a foundation for future AI authoring tools that harmonize generative power with human professional expertise.