Apprentice Tutor Builder
This project sits at the intersection of HCI and Human-Like AI models and the goal is to design teachable agents using Hierarchical Task Networks (HTNs) that can learn from humans in a multi-modal and incremental way. These apprentice agents are provided primitive operators that define fundamental knowledge of actions the agent can take. These actions contain conditions that define when the actions can be applied given a particular configuration of the task environment. Learning is achieved by constructing methods that solve tasks, decomposing higher-level tasks into subtasks, and refining the conditions on methods to determine when they can be applied. These agents will serve as the underlying AI and be integrated into other ongoing projects, including our Apprentice Tutors and HMT gaming work.
Related Publications
Glen Smith, Christopher J. MacLellan (2025). L.E.A.R.N: A Hybrid Architecture for Language-Guided Induction of Hierarchical Task Networks. Proceedings of the Twelfth Annual Conference on Advances in Cognitive Systems.
Tommaso Calò, Christopher J. MacLellan (2024). Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfaces. Proceedings of the Eleventh ACM Conference on Learning @ Scale.
Glen Smith, Adit Gupta, Christopher J. MacLellan (2024). Apprentice Tutor Builder: A Platform For Users to Create and Personalize Intelligent Tutors. arXiv preprint.
Christopher J. MacLellan, Kenneth R. Koedinger (2020). Domain General Tutor Authoring with Apprentice Learner Models. International Journal of Artificial Intelligence in Education.