25 citations · 26 across the 12 of their papers we have counts for
13 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…
CORGI: Efficient Pattern Matching With Quadratic Guarantees
Daniel Weitekamp
Rule-based systems must solve complex matching problems within tight time constraints to be effective in real-time applications, such as planning and reactive control for AI agents…
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…
TutorGym: A Testbed for Evaluating AI Agents as Tutors and Students
Daniel Weitekamp, Momin N. Siddiqui, Christopher J. MacLellan
Recent improvements in large language model (LLM) performance on academic benchmarks, such as MATH and GSM8K, have emboldened their use as standalone tutors and as simulations of h…
Beyond Final Answers: Evaluating Large Language Models for Math Tutoring
Adit Gupta, Jennifer Reddig, Tommaso Calo +2
Researchers have made notable progress in applying Large Language Models (LLMs) to solve math problems, as demonstrated through efforts like GSM8k, ProofNet, AlphaGeometry, and Mat…
AI2T: Building Trustable AI Tutors by Interactively Teaching a Self-Aware Learning Agent
Daniel Weitekamp, Erik Harpstead, Kenneth Koedinger
AI2T is an interactively teachable AI for authoring intelligent tutoring systems (ITSs). Authors tutor AI2T by providing a few step-by-step solutions and then grading AI2T's own pr…
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
Liang Zhang, Jionghao Lin, John Sabatini +6
Learning performance data describe correct and incorrect answers or problem-solving attempts in adaptive learning, such as in intelligent tutoring systems (ITSs). Learning performa…
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…
Decomposed Inductive Procedure Learning
Daniel Weitekamp, Christopher MacLellan, Erik Harpstead +1
Recent advances in machine learning have made it possible to train artificially intelligent agents that perform with super-human accuracy on a great diversity of complex tasks. How…
Topology classification with deep learning to improve real-time event selection at the LHC
Thong Q. Nguyen, Daniel Weitekamp, Dustin Anderson +5
We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consi…