# CogGen: A Learner-Centered Generative AI Architecture for Intelligent Tutoring with Programming Video

arXiv: https://arxiv.org/abs/2506.20600 · HTML: https://mlsanity.com/paper/2506.20600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principles](https://mlsanity.com/paper/2305.13272/) (2023)
- [How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging](https://mlsanity.com/paper/2310.05292/) (2023)
- [CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues](https://mlsanity.com/paper/2404.03820/) (2024)
- [pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models](https://mlsanity.com/paper/2105.00385/) (2021)
- [Human-AI Collaboration Increases Skill Tagging Speed but Degrades Accuracy](https://mlsanity.com/paper/2403.02259/) (2024)
- [A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge](https://mlsanity.com/paper/2307.07544/) (2023)

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

- [T2MM: An LLM Supported Architecture For Inquiry-Based Modeling](https://mlsanity.com/paper/2606.11210/) (2026)

## Abstract (arXiv)

We introduce CogGen, a learner-centered AI architecture that transforms programming videos into interactive, adaptive learning experiences by integrating student modeling with generative AI tutoring based on the Cognitive Apprenticeship framework. The architecture consists of three components: (1) video segmentation by learning goals, (2) a conversational tutoring engine applying Cognitive Apprenticeship strategies, and (3) a student model using Bayesian Knowledge Tracing to adapt instruction. Our technical evaluation demonstrates effective video segmentation accuracy and strong pedagogical alignment across knowledge, method, action, and interaction layers. Ablation studies confirm the necessity of each component in generating effective guidance. This work advances AI-powered tutoring by bridging structured student modeling with interactive AI conversations, offering a scalable approach to enhancing video-based programming education.