This project will develop and evaluate a large language model (LLM) companion that students will use to enhance their learning experience. Unlike most AI systems that engage in some form of tutoring or question answering, the idea will be to flip the roles. The LLM agent will act as an ignorant virtual student and the human student will be responsible for teaching concepts to the LLM companion. The human student will effectively learn by explaining concepts to the LLM–a key element of effective learning and probing one’s knowledge gaps. Plus, it could be a lot more interesting and engaging than traditional forms of learning. We will design a special exam to test the concepts learned by the LLM companion, indirectly assessing whether the human student also knows those concepts. In a randomized control trial in the classroom, we will further assess student self-efficacy, conceptual knowledge, and general engagement in the class.