DeepTutor, funded by the Institute for Education Sciences, is an advanced intelligent tutoring system that fosters students' deep understanding of complex science topics through quality interaction and instruction. Quality interaction means the student and tutor understand each other at every turn and over many dialogue turns. Quality interaction is possible in DeepTutor through the use of a novel, state-of-the-art natural language-based knowledge representation, called the latent semantic logic form or shortly the semantic logic form (SLF), and advanced dialogue management techniques that embed novel conversational goals such as perfect grounding at every turn.

Quality instruction in DeepTutor is assured by the use of Learning Progressions (LPs), a recently developed framework by the science education research community to describe students' natural paths to mastery. LPs capture the natural sequence of mental models and mental model shifts students go through while mastering a topic. LPs are critical components in our proposed advanced methods for handling core tasks in DeepTutor: modeling the task domain, tracking students’ knowledge states, and providing appropriate feedback. Advances in these core tutoring tasks will enable a highly-adaptive DeepTutor system. Fully adaptive tutoring is desirable because it maximizes tutees learning through strategies customized to each individual student.

For any comments or questions please contact Dr. Vasile Rus (vrus@memphis.edu).