Publications
Weitekamp, D., & Koedinger, K. [Manuscript under review] Computational models of learning: deepening care and carefulness in AI in education with Theory-Driven Simulation. International Journal of Artificial Intelligence in Education
Weitekamp, D., CORGI: Efficient Pattern Matching With Quadratic Guarantees. Advances in Cognitive Systems. (pdf)
Weitekamp, D., N. Siddiqui, M., & J. MacLellan, C. (2025). TutorGym: A Testbed for Evaluating AI Agents as Tutors and Students. International Conference on Artificial Intelligence in Education, 361–376. Springer Nature Switzerland Cham. (pdf)
Gupta, A., Reddig, J., Calo, T., Weitekamp, D., & MacLellan, C. J. (2025). Beyond final answers: Evaluating large language models for math tutoring. International Conference on Artificial Intelligence in Education, 323–337. Springer Nature Switzerland Cham. (link)
Weitekamp, D., MacLellan, C., Harpstead, E., & Koedinger, K. (2025). Decomposed inductive procedure learning: Learning academic tasks with human-like data efficiency. Proceedings of the Annual Meeting of the Cognitive Science Society, 47. (pdf) (link)
Weitekamp, D., Harpstead, E., & Koedinger, K. (2024). AI2T: Building trustable AI tutors by interactively teaching a self-aware learning agent. arXiv Preprint arXiv:2411.17924. (pdf) (arxiv)
Rachatasumrit, N., Weitekamp, D. [equal contrib.], & Koedinger, K. R. (2024). Good Fit Bad Policy: Why Fit Statistics Are a Biased Measure of Knowledge Tracer Quality. International Conference on Artificial Intelligence in Education, 183–191. Springer Nature Switzerland Cham. [Best Paper Nominee 🥈] (pdf) (link)
Weitekamp, D., & Koedinger, K. (2024). STAND: Data-Efficient and Self-Aware Precondition Induction for Interactive Task Learning. arXiv Preprint arXiv:2409.07653. (pdf) (arxiv)
Weitekamp, D., & Koedinger, K. (2023). Computational models of learning: deepening care and carefulness in AI in education. International Conference on Artificial Intelligence in Education, 13–25. Springer Nature Switzerland Cham. (pdf) (link)
Weitekamp, D., Rachatasumrit, N., Wei, R., Harpstead, E., & Koedinger, K. (2023). Simulating learning from language and examples. International Conference on Artificial Intelligence in Education, 580–586. Springer Nature Switzerland Cham. (pdf full) (pdf short) (link)
Weitekamp, D., & Stevens, P. (2022). A Mobile Invented Spelling Tutoring System. International Conference on Artificial Intelligence in Education, 492–496. Springer International Publishing Cham. (pdf) (link)
Weitekamp, D., Harpstead, E., & Koedinger, K. (2021). Toward stable asymptotic learning with simulated learners. International Conference on Artificial Intelligence in Education, 390–394. Springer International Publishing Cham. (pdf full) (pdf short) (link)
Weitekamp, D., Harpstead, E., & Koedinger, K. R. (2020). An interaction design for machine teaching to develop AI tutors. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–11. (pdf) (link)
Weitekamp, D., Ye, Z., Rachatasumrit, N., Harpstead, E., & Koedinger, K. (2020). Investigating differential error types between human and simulated learners. International Conference on Artificial Intelligence in Education , 586–597. Springer International Publishing Cham. (pdf) (link)
Harpstead, E., MacLellan, C. J., Weitekamp, D., & Koedinger, K. R. (n.d.). (2019) The use of simulated learners in adaptive education. AIAED-19: AI+ Adaptive Education, 1–3. (pdf)
Weitekamp, D., III, Harpstead, E., MacLellan, C. J., Rachatasumrit, N., & Koedinger, K. R. (2018). Toward Near Zero-Parameter Prediction Using a Computational Model of Student Learning. International Conference on Educational Data Mining (EDM) 2018. (pdf) (link)
PhD Dissertation
Weitekamp, D., (2024). Building Educational Technology Quickly and Robustly with an Interactively Teachable AI. Doctoral dissertation, Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburg, PA. (pdf)