Advancing Education through Tutoring Systems: A Systematic Literature Review

2025-03-12

AI Summary

This study addresses the global challenges of insufficient student proficiency in core academic subjects and scarce personalized instructional resources. Systematically reviewing 86 studies under the PRISMA framework, it conducts an integrative analysis of AI-driven intelligent tutoring systems (ITS) and robotic tutoring systems (RTS). Methodologically, it integrates Bayesian knowledge tracing, large language models, and educationally grounded empirical modeling. A novel contribution is the first application of latent class analysis (LCA) to categorize such systems into three distinct types: computer-based ITS, robot-based RTS, and multimodal hybrid systems—thereby proposing a new complementary hybrid architecture paradigm. Results demonstrate that AI-powered tutoring systems significantly enhance instructional adaptivity and learning outcomes. Concurrently, the study identifies critical challenges—including ethical risks, scalability limitations, and weak cognitive modeling foundations—providing both theoretical grounding and a practical roadmap for next-generation educational AI systems.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

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.

Problem

• Addressing global educational challenges through advanced tutoring systems.
• Enhancing personalized and adaptive instruction using AI and robotics.
• Identifying and overcoming scalability, ethical, and cognitive adaptability issues.

Innovation

• AI models enhance personalized cognitive support
• Robot systems improve social-emotional engagement
• Hybrid solutions integrate ITS and RTS strengths