1 citations · 1 across the 5 of their papers we have counts for
6 papers
Structure-Mapping-Guided Self-Explanation for Learning Mathematical Procedures
Shinhaeng Lee, Christopher J. MacLellan, Daniel Weitekamp
Worked examples are a powerful form of instruction, but learners must infer how the demonstrated steps were produced. A naive simulation of this self-explanation process can genera…
SMTB: Fast Structure-Mapping with Tight Bounds
Daniel Weitekamp, Christopher MacLellan
Structure-mapping forms analogies by aligning systems of relationally connected elements based on shared structure instead of surface features. We introduce a new structure-mapping…
Guidelines for Designing AI Technologies to Support Adult Learning
Jennifer M. Reddig, Glen R. Smith, Sanaz Ahmadzadeh Siyahrood +16
AI-powered educational technologies have demonstrated measurable benefits for learners, but their design and evaluation have largely centered on K-12 contexts. As a result, many AI…
When Should Users Check? Modeling Confirmation Frequency in Multi-Step Agentic AI Tasks
Jieyu Zhou, Aryan Roy, Sneh Gupta +2
Existing AI agents typically execute multi-step tasks autonomously and only allow user confirmation at the end. During execution, users have little control, making the confirm-at-e…
Decomposed Inductive Procedure Learning: Learning Academic Tasks with Human-Like Data Efficiency
Daniel Weitekamp, Christopher MacLellan, Erik Harpstead +1
Human learning relies on specialization -- distinct cognitive mechanisms working together to enable rapid learning. In contrast, most modern neural networks rely on a single mechan…
STAND: Self-Aware Precondition Induction for Interactive Task Learning
Daniel Weitekamp, Glen Smith, Kenneth Koedinger +1
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 pre…