This tutorial examines the integration of Theory of Mind (ToM) into AI-driven tutoring systems, with a focus on how large language models (LLMs) can represent learners’ cognitive and emotional states to enable adaptive, personalized feedback. Participants will learn foundational ToM concepts from cognitive science and psychology and how these ideas can be operationalized in AI systems. We discuss mutual ToM, in which both tutors and learners model each other’s mental states, and address challenges including misconception detection, metacognitive modeling, and privacy in data-driven tutoring. The tutorial also includes hands-on demonstrations of machine ToM in programming education using benchmark datasets such as CS1QA and CodeQA. By combining theoretical foundations, empirical insights, and practical exercises, this tutorial will provide an overview of designing human-centered, ethically aware, and cognitively informed AI tutoring systems.