Advancing Education through Tutoring Systems: A Systematic Literature Review
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.
Used in syntheses
Questions this source addresses
- How does the timing of practice change what we remember?
- When does mixing different kinds of practice help us learn?
- When should an intelligent tutor make learning harder?
- Why do some things we learn stay with us?
- How do Intelligent Tutoring Systems and Robot Tutoring Systems complement each other in delivering personalized, adaptive instruction versus social-emotional engagement?
- What AI techniques, such as Bayesian Knowledge Tracing and Large Language Models, are driving improvements in adaptability and learning outcomes within tutoring systems?
- What distinct categories of tutoring systems emerge from a Latent Class Analysis of 86 studies on Intelligent Tutoring Systems and Robot Tutoring Systems?
- What ethical, scalability, and cognitive-adaptability gaps remain unresolved in current tutoring system research, and how might hybrid ITS-RTS solutions address them?