Abstract:

Computer tutors have made significant progress since they made their first appearance in the late 1960's. The application of artificial intelligence (AI) techniques led to a "second generation" of intelligent tutoring systems (ITSs) that were able to model cognitive skills, give individualized feedback, and track learning with greater precision. In the late 1990's, ITSs emerged that were able to carry out meaningful dialogues with students in order to promote learning. These modern systems leverage advances in the field of natural language processing and dialogue systems. In this talk, I will present my research on building ITSs that use natural language techniques to address the metacognitive skills of planning and reflection. The first part of the talk will focus on a dialogue-based tutoring system for novice programmers that I developed at the University of Pittsburgh that supports basic problem solving and planning skills. In an evaluation, the primary findings were that students who received tutoring from the ITS exhibited an improved ability to compose plans and displayed behaviors suggestive of thinking at greater levels of abstraction than students in a read-only control group. The second part of the talk will provide of an overview of my more recent work conducted at the USC Institute for Creative Technologies. This research effort focuses on the use of explainable AI, natural language generation, and the development of a model of reflective tutoring to support learning of complex skills in game-based learning environments. Here, we consider the nature of learning in loosely-defined domains, serious games, and how tutoring can be used to promote productive learning behaviors.