Abstract
Intelligent tutoring systems (ITSs) are computer programs that provide instruction adapted to the needs of individual students. Dialog systems are computer programs that communicate with human users by using natural language. This paper presents a systematic literature review to address ITSs that incorporate dialog systems and have been implemented in the last twenty years. The review found 33 ITSs and focused on answering the following five research questions. a) What ITSs with natural language dialogue have been developed? b) What is the main purpose of the tutoring dialogue in each system? c) What are the pedagogical features of the teaching process performed by the ITSs with natural language dialogue? d) What natural language understanding approach does each system employ to understand students' utterances? e) What evidence exists related to the evaluation of ITSs with natural language dialogue? The results of this review reveal that most ITSs are directed toward science, technology, engineering, and mathematics (STEM) domains at the university level, and the majority of the selected ITSs implement the expectations and misconceptions tailored approach. Furthermore, most ITSs use dialog to help students learn how to solve a problem by applying rules, laws, etc. (the apply level in Bloom's taxonomy). With regard to the instructional approach, the selected ITSs help students write correct explanations or answers for deep questions; assist students in problem solving; or support a reflective dialogue motivated by either previously provided content or the result of a simulation. Additionally, we found empirical evaluations for 90.91% of the selected ITSs that measure the learning gains and/or assess the impacts of different tutoring strategies.
Highlights
Intelligent tutoring systems (ITSs) are computer programs that provide instruction adapted to the needs of individual students; i.e., they perform functions inherent to the tutorial process to cause a cognitive and motivational change in the student
We have considered the ITS types mentioned in [4]: model-tracing tutors (MTTs), constraint-based modeling (CBM), Bayesian network modeling (BNM), and expectation and misconceptions tailoring (EMT)
The results indicated that even though the students produced the same percentage of content talk under the two human-computer conditions, the proportion of content talk was correlated only with learning gain in the condition similar to ELICIT
Summary
Intelligent tutoring systems (ITSs) are computer programs that provide instruction adapted to the needs of individual students; i.e., they perform functions inherent to the tutorial process (presenting information that must be learned, asking questions or assigning tasks, providing feedback, etc.) to cause a cognitive and motivational change in the student. To accomplish this goal, ITSs leverage artificial intelligence techniques to define content models (the subject to be taught) as well as the tutoring strategies to be employed with each student; i.e., they specify ‘‘what’’ and ‘‘how’’ to teach [1]. Dialog systems are currently gaining interest in different fields of application, such as e-commerce, personal assistants, and call centers
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