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My work aims to improve our understanding of how people teach and learn and to build AI systems that can teach and learn like people do. I explore the development of computational models of human learning and how these models can support the development of effective learning technologies at scale.
I teach the following two classes:
CS 4635/7637 – Knowledge-Based AI (Fall)
CS 8803 – Human-AI Interaction (Spring)
MacLellan, C.J. (2025). Model Human Learners: Computational Models to Guide Instructional Design. In Proceedings of the Annual Conference of the Cognitive Science Society.
Wang, Z., Harrer, E., Zhu, T., Dai, Z., & MacLellan, C. J. (2025). Deep Taxonomic Networks for Unsupervised Hierarchical Prototype Discovery. In Proceedings of Advances in Neural Information Processing Systems.
Barari, N., Lian, X., and MacLellan, C.J. (2026). Robust Incremental Learning of Visual Concepts without Catastrophic Forgetting. Cognitive Systems Research.
Lawley, L., & MacLellan, C. J. (2024). VAL: Interactive Task Learning with GPT Dialog Parsing. In Proceedings of the CHI Conference on Human Factors in Computing Systems.
Zhou, J., Roy, A., Gupta, S., Weitekamp, D., & MacLellan, C.J. (2026). When Should Users Check? Modeling Confirmation Frequency in Multi-Step Agentic AI Tasks. In Proceedings of the CHI Conference on Human Factors in Computing Systems.
Gupta, A., Reddig, J., Caló, T., Weitekamp, D., & MacLellan, C.J. (2025). Beyond Final Answers: Evaluating Large Language Models for Math Tutoring. In Proceedings of the 26th International Conference on Artificial Intelligence in Education.
Zhang, Q., Smith, G., Li, Z., Dong, Y., Harpstead, E., & Maclellan, C. (2025). Dice Adventure: An Asymmetrical Collaborative Game for Exploring the Hybrid Teaming Effects. In Proceedings of the 20th International Conference on the Foundations of Digital Games.