From Expert Systems to Intelligent Tutoring Systems

1992 ◽  
pp. 149-161 ◽  
Author(s):  
Claude Frasson
Keyword(s):  
Expert Systems ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems

Combining Expert Systems and Multiple Intelligences in an Adaptive and Intelligent Tutoring System

2013 ◽  
Vol 8 (3) ◽  
pp. 22-33
Author(s):  
Hafidi Mohamed ◽  
Bensebaa Taher
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Multiple Intelligences ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
The Other ◽  
Tutoring System ◽  
Tutoring Systems ◽  
Intellectual Abilities

This paper describes an adaptive and intelligent tutoring system (AITS) based on multiple intelligences and expert system. Most of adaptive and intelligent tutoring systems based their adaptation to user’s skill level. Other learner features taken into account are background, hyperspace experience, preferences and interests. However, less attention was paid to multiple intelligences and their effects on learning. Moreover, to design AITS which can manage both different disciplinary domains and a guide for the learner is difficult. The specialization of the analysis treatments is responsible for the loss of reusability for the other disciplinary domains. To overcome these limitations, the authors will try to combine the benefits of paradigms (adaptive hypermedia, intelligent tutoring system, multiple intelligences) in order to adapt the course to the needs and intellectual abilities of each learner.

scholarly journals Incorporation of Artificial Intelligence Tutoring Techniques in Mathematics

2016 ◽  
Vol 6 (4) ◽  
pp. 12 ◽  
Author(s):  
Marios Pappas ◽  
Athanasios Drigas
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Mathematics Education ◽  
Expert Systems ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Personalized Feedback ◽  
Tutoring Systems ◽  
Artificial Intelligence Techniques ◽  
Human Tutor

Intelligent Tutoring Systems incorporate Artificial Intelligence techniques, in order to imitate a human tutor. These expert systems are able to assess student’s proficiency, to provide solved examples and exercises for practice in each topic, as well as to provide immediate and personalized feedback to learners. The present study is a systematic review that evaluates the contribution of the Intelligent Tutoring Systems developed so far, to Mathematics Education, representing some of the most representative studies of the last decade.

Intelligent tutoring systems for aviation automation

2000 ◽  
Author(s):  
Christine Mitchel ◽  
Alan Chappell ◽  
W. Gray ◽  
Alex Quinn ◽  
David Thurman
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

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