Advancing Education through Tutoring Systems: A Systematic Literature Review

TL;DR

This systematic review examines how tutoring systems, including AI-driven Intelligent Tutoring Systems and Robot Tutoring Systems, are transforming education by providing personalized, engaging, and adaptive learning experiences, while also discussing current challenges and future directions.

Contribution

It categorizes tutoring systems into three types using Latent Class Analysis and evaluates recent technological and pedagogical advancements, highlighting the need for integrated hybrid solutions.

Findings

AI techniques improve adaptability and engagement.

Significant progress in learning outcomes observed.

Challenges include ethical concerns and scalability issues.

Abstract

This study systematically reviews the transformative role of Tutoring Systems, encompassing Intelligent Tutoring Systems (ITS) and Robot Tutoring Systems (RTS), in addressing global educational challenges through advanced technologies. As many students struggle with proficiency in core academic areas, Tutoring Systems emerge as promising solutions to bridge learning gaps by delivering personalized and adaptive instruction. ITS leverages artificial intelligence (AI) models, such as Bayesian Knowledge Tracing and Large Language Models, to provide precise cognitive support, while RTS enhances social and emotional engagement through human-like interactions. This systematic review, adhering to the PRISMA framework, analyzed 86 representative studies. We evaluated the pedagogical and technological advancements, engagement strategies, and ethical considerations surrounding these systems. Based on these parameters, Latent Class Analysis was conducted and identified three distinct categories: computer-based ITS, robot-based RTS, and multimodal systems integrating various interaction modes. The findings reveal significant advancements in AI techniques that enhance adaptability, engagement, and learning outcomes. However, challenges such as ethical concerns, scalability issues, and gaps in cognitive adaptability persist. The study highlights the complementary strengths of ITS and RTS, proposing integrated hybrid solutions to maximize educational benefits. Future research should focus on bridging gaps in scalability, addressing ethical considerations comprehensively, and advancing AI models to support diverse educational needs.