Intelligent tutoring system

About: Intelligent tutoring system is a research topic. Over the lifetime, 3472 publications have been published within this topic receiving 58217 citations. The topic is also known as: ITS.

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 21st 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.

Gaze tutor: A gaze-reactive intelligent tutoring system

Abstract: We developed an intelligent tutoring system (ITS) that aims to promote engagement and learning by dynamically detecting and responding to students' boredom and disengagement. The tutor uses a commercial eye tracker to monitor a student's gaze patterns and identify when the student is bored, disengaged, or is zoning out. The tutor then attempts to reengage the student with dialog moves that direct the student to reorient his or her attentional patterns towards the animated pedagogical agent embodying the tutor. We evaluated the efficacy of the gaze-reactive tutor in promoting learning, motivation, and engagement in a controlled experiment where 48 students were tutored on four biology topics with both gaze-reactive and non-gaze-reactive (control condition) versions of the tutor. The results indicated that: (a) gaze-sensitive dialogs were successful in dynamically reorienting students' attentional patterns to the important areas of the interface, (b) gaze-reactivity was effective in promoting learning gains for questions that required deep reasoning, (c) gaze-reactivity had minimal impact on students' state motivation and on self-reported engagement, and (d) individual differences in scholastic aptitude moderated the impact of gaze-reactivity on overall learning gains. We discuss the implications of our findings, limitations, future work, and consider the possibility of using gaze-reactive ITSs in classrooms.

Evaluation of a Constraint-Based Tutor for a Database Language

Abstract: We propose a novel approach to intelligent tutoring in which feedback messages are associated with constraints on correct problem solution. The knowledge state of the student is represented by the constraints that he or she does and does not violate during problem solving. Constraint-based tutoring has been implemented in SQL-Tutor, an intelligent tutoring system for teaching the database query language SQL. Empirical evaluation shows that (a) students find the system easy to use, and (b) they do better on a subsequent classroom examination than peers without experience with the system. Furthermore, learning curves are smooth when plotted in terms of individual constraints, supporting the psychological appropriateness of the constraint construct.

Learning Strategies and Transfer in the Domain of Programming

Abstract: We report two studies involving an intelligent tutoring system for Lisp (the Camegie Mellon University Lisp Tutor). In Experiment 1, we developed a model, based on production system theories of transfer and analogical problem solving, that accounts for effects of instructional examples, the transfer of cognitive skills across programming problems, and practice effects. In Experiment 2, we analyzed protocols collected from subjects as they processed instructional texts and examples before working with the Lisp Tutor and protocols collected after subjects solved each programming problem. The results suggest that the acquisition of cognitive skills is facilitated by high degrees of metacognition, which includes higher degrees of monitoring states of knowledge, more self-generated explanation goals and strategies, and greater attention to the instructional structure. Improvement in skill acquisition is also strongly related to the generation of explanations connecting the example material to the abstract term...

Redefining the learning companion: the past, present, and future of educational agents

Abstract: The development of intelligent tutoring systems has long been the focus of applying artificial intelligence and cognitive science in education. A new breed of intelligent learning environments called learning companion systems was developed over a decade ago. In contrast to an intelligent tutoring system, in which a computer mimics an intelligent tutor, the learning companion system assumes two roles, one as an intelligent tutor and another as a learning companion. Motivated by recent interest in agent research and other technologies, this learning companion field has received increasing attention. This study addresses issues that arise from different perspectives on this research effort. With a view to future networked learning environments, the learning companion is redefined for application to a wide spectrum of educational agent research. Accordingly, several subjects that relate to educational agents, and hence learning companions, are identified.