Ken Koedinger — accepted AI-conference papers

2026

STAND: Self-Aware Precondition Induction for Interactive Task Learning

Daniel Weitekamp, Glen Smith, Ken Koedinger, Christopher MacLellan

ICML 2026poster

In interactive task learning (ITL), AI agents learn new capabilities from limited human instruction provided during task execution. STAND is a new method of data-efficient rule precondition induction specifically designed for these human-in-the-loop training scenarios. A key feature of STAND is its …

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2025

How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging (Extended Abstract)

Qianou Ma, Hua Shen, Ken Koedinger, Tongshuang Wu

IJCAI 2025

Large Language Models (LLMs) excel at generating content at impeccable speeds. However, they are imperfect and still make various mistakes. In Computer Science education, as LLMs are widely recognized as "AI pair programmers," it becomes increasingly important to train students on evaluating and deb

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