Deep Learning in Intelligent Tutoring Systems (ITSs): a systematic literature review

Abstract

This article presents a systematic literature review on the recent advancements in Intelligent Tutoring Systems (ITS) using deep learning for personalized instruction. The study applied the quantitative Methodi Ordinatio method, selecting 22 relevant articles in the educational field. The results highlight the countries, journals, and areas with the highest number of publications on the subject, as well as the most prolific and cited authors. The deep learning technologies utilized and the applications presented in the articles were also mapped. The research examined the effectiveness of the technology, seeking evidence of improved learning outcomes, and analyzed the grounding in pedagogical theories. This study provides an updated overview of the state-of-the-art in applying deep learning to ITS, contributing to scientific knowledge in this field. The results emphasize the contemporary relevance of the topic and the growing interest in enhancing personalized teaching approaches. This research offers valuable insights for researchers, educators, and professionals interested in understanding and implementing the use of deep learning in ITS, aiming to improve students' learning experience and outcomes.