Intelligent Tutoring Systems: A Review

Intelligent Tutoring Systems (ITSs) are advanced educational technologies that incorporate Artificial Intelligence (AI) techniques to offer instruction adapted to the student’s individual needs. The main contribution of this paper is to provide an up-to-date understanding of the integration of AI techniques in ITSs by conducting a systematic review. The review focused on answering the following research questions: What AI techniques are integrated into ITSs? What are the objectives of employing AI techniques in ITSs? What are students’ specific attributes associated with ITSs? Research papers from 2021 to 2023 (the last three years) were extracted from the Scopus database, and 21 papers were included in the study based on the inclusion and exclusion criteria. The systematic review was conducted based on the PRISMA 2020 statement. The study reveals that Machine Learning is the predominant category deployed in the reviewed ITSs, followed by Natural Language Processing (NLP) and Bayesian techniques. The primary objectives motivating the adoption of AI include predicting student performance and generating adaptive feedback/hints. Notably, student characteristics such as performance, behavior, and prior knowledge emerge as crucial considerations within ITSs.