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The Evidence Base on AI in K-12: A 2026 Review

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

Authors
Wengxi Li,
Roy Pea,
Nick Haber,
Hariharan Subramonyam
Date
Publisher
arXiv
Link
http://arxiv.org/pdf/2506.20600v1
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.
Study at a glance
  • Question: How can generative AI transform programming videos into interactive, personalized tutoring experiences?
  • Setting and data: Not stated in the abstract
  • Method: Technical evaluation and testing of system components (ablation studies)
Why it matters

Transforming programming videos into interactive, adaptive learning experiences requires scalable approaches to instruction. Bridging structured student modeling with conversational generative AI tutoring addresses this need for learner-centered, video-based programming education.

Key findings
  • In video-based programming education, an AI tutoring system accurately divided instructional videos into segments based on specific learning goals.
  • The AI tutoring system demonstrated strong pedagogical alignment across its content knowledge, instructional methods, actions, and conversational interactions.
  • Testing confirmed that video segmentation, conversational tutoring, and tracking student knowledge were all necessary for the AI to generate effective guidance.

Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.

What is the application?
Learning – Student Support
Who is the user?
Student
Who age?
Post-Secondary,
Adult
Why use AI?
Outcomes – Other Academic,
Outcomes – Differentiation
Study design
Descriptive – Product Development,
Technical – Computational
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