One essential goal in education is to improve understanding of how humans acquire knowledge and how students vary in their abilities to learn. Building an intelligent agent that models student learning would be a significant achievement in the learning sciences. SimStudent is a state-of-the-art intelligent agent that simulates a human's learning process. However, SimStudent has only been living in the world of graphical user interfaces. To construct a more human-like learning agent, we integrate SimStudent with a cognitive robot, Calliope5KP, to create a physical agent that is able to learn skill knowledge by interacting with users in the physical world. We demonstrate the integration in a tic-tac-toe game, and show that the SimStudent robot is able to learn reasonably well with 12 games.

Building an intelligent agent, which simulates human-level learning appropriate for learning math, science, or a second language, could potentially benefit both education in understanding human learning, and artificial intelligence in creating human-level intelligence. Recently, we have proposed an efficient approach to acquiring procedural knowledge using transfer learning. However, it operated as a separate module. In this paper, we describe how to integrate this module into a machine-learning agent, SimStudent, that learns procedural knowledge from examples and through problem solving. We illustrate this method in the domain of algebra, after which we consider directions for future research in this area.