Toward Near Zero-Parameter Prediction Using a Computational Model of Student Learning. - Daniel Weitekamp III; Erik Harpstead; Christopher J. MacLellan; Napol Rachatasumrit; Kenneth R. Koedinger (EDM, 2019)

Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC. - Thong Q. Nguyen; Daniel Weitekamp III; Dustin Anderson; Roberto Castello; Olmo Cerri; Maurizio Pierini; Maria Spiropulu; Jean-Roch Vlimant (Comput. Softw. Big Sci., 2019)

Investigating Differential Error Types Between Human and Simulated Learners. - Daniel Weitekamp III; Zihuiwen Ye; Napol Rachatasumrit; Erik Harpstead; Kenneth R. Koedinger (AIED (1), 2020)

An Interaction Design for Machine Teaching to Develop AI Tutors. - Daniel Weitekamp III; Erik Harpstead; Kenneth R. Koedinger (CHI, 2020)

Toward Stable Asymptotic Learning with Simulated Learners. - Daniel Weitekamp III; Erik Harpstead; Kenneth R. Koedinger (AIED (2), 2021)

Decomposed Inductive Procedure Learning. - Daniel Weitekamp III; Christopher MacLellan; Erik Harpstead; Kenneth R. Koedinger (CoRR, 2021)

A Mobile Invented Spelling Tutoring System. - Daniel Weitekamp III; Patience Stevens (AIED (2), 2022)

Building Educational Technology Quickly and Robustly with an Interactively Teachable AI. - Daniel Weitekamp III (AIED (Posters/Late Breaking Results/...), 2023)

Computational Models of Learning: Deepening Care and Carefulness in AI in Education. - Daniel Weitekamp III; Kenneth R. Koedinger (AIED (Posters/Late Breaking Results/...), 2023)

Simulating Learning from Language and Examples. - Daniel Weitekamp III; Napol Rachatasumrit; Rachael Wei; Erik Harpstead; Kenneth R. Koedinger (AIED (Posters/Late Breaking Results/...), 2023)

Decomposed Inductive Procedure Learning: Learning Academic Tasks with Human-Like Data Efficiency. - Daniel Weitekamp III (AAAI Spring Symposia, 2024)

Good Fit Bad Policy: Why Fit Statistics Are a Biased Measure of Knowledge Tracer Quality. - Napol Rachatasumrit; Daniel Weitekamp III; Kenneth R. Koedinger (AIED Companion (2), 2024)

STAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning. - Daniel Weitekamp III; Kenneth R. Koedinger (CoRR, 2024)

AI2T: Building Trustable AI Tutors by Interactively Teaching a Self-Aware Learning Agent. - Daniel Weitekamp III; Erik Harpstead; Kenneth R. Koedinger (CoRR, 2024)

Beyond Final Answers: Evaluating Large Language Models for Math Tutoring. - Adit Gupta; Jennifer M. Reddig; Tommaso Calò; Daniel Weitekamp III; Christopher J. MacLellan (AIED (1), 2025)

TutorGym: A Testbed for Evaluating AI Agents as Tutors and Students. - Daniel Weitekamp III; Momin Naushad Siddiqui; Christopher J. MacLellan (AIED (3), 2025)

Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI. - Liang Zhang; Jionghao Lin; John Sabatini 001; Conrad Borchers; Daniel Weitekamp III; Meng Cao; John Hollander; Xiangen Hu; Arthur C. Graesser (IEEE Trans. Learn. Technol., 2025)

Decomposed Inductive Procedure Learning: Learning Academic Tasks with Human-Like Data Efficiency. - Daniel Weitekamp III; Christopher MacLellan; Erik Harpstead; Kenneth R. Koedinger (CoRR, 2025)