Recent Advances in Conversational Intelligent Tutoring Systems

We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress In terms of macro- and microadaptivity. Macroadaptivitp refers to a system's capability to select appropriate instructional tasks for the learner to work on. Micro gdaptivity refers to a system's capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro- and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue, and natural languaage-processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language-processing techniques are key features of Deep-Tutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student, which is needed for maximizing engagement and ultimately for learning.