AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back

Abstract

We present AutoTutor and Affective AutoTutor as examples of innovative 21 st century interactive intelligent systems that promote learning and engagement. AutoTutor is an intelligent tutoring system that helps students compose explanations of difficult concepts in Newtonian physics and enhances computer literacy and critical thinking by interacting with them in natural language with adaptive dialog moves similar to those of human tutors. AutoTutor constructs a cognitive model of students' knowledge levels by analyzing the text of their typed or spoken responses to its questions. The model is used to dynamically tailor the interaction toward individual students' zones of proximal development. Affective AutoTutor takes the individualized instruction and human-like interactivity to a new level by automatically detecting and responding to students' emotional states in addition to their cognitive states. Over 20 controlled experiments comparing AutoTutor with ecological and experimental controls such reading a textbook have consistently yielded learning improvements of approximately one letter grade after brief 30--60-minute interactions. Furthermore, Affective AutoTutor shows even more dramatic improvements in learning than the original AutoTutor system, particularly for struggling students with low domain knowledge. In addition to providing a detailed description of the implementation and evaluation of AutoTutor and Affective AutoTutor, we also discuss new and exciting technologies motivated by AutoTutor such as AutoTutor-Lite, Operation ARIES, GuruTutor, DeepTutor, MetaTutor, and AutoMentor. We conclude this article with our vision for future work on interactive and engaging intelligent tutoring systems.