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.

翻译：本研究系统性地综述了辅导系统（包括智能辅导系统（ITS）和机器人辅导系统（RTS））如何通过先进技术应对全球教育挑战所发挥的变革性作用。由于许多学生在核心学术领域的熟练度上存在困难，辅导系统通过提供个性化和自适应的教学来弥合学习差距，成为一种前景广阔的解决方案。ITS利用人工智能（AI）模型（如贝叶斯知识追踪和大语言模型）提供精确的认知支持，而RTS则通过类人交互增强社交和情感参与度。本系统性综述遵循PRISMA框架，分析了86项代表性研究。我们评估了围绕这些系统的教学与技术进展、参与策略以及伦理考量。基于这些参数，我们进行了潜在类别分析，并识别出三个不同的类别：基于计算机的ITS、基于机器人的RTS以及整合了多种交互模式的多模态系统。研究结果表明，AI技术在增强适应性、参与度和学习成果方面取得了显著进展。然而，诸如伦理关切、可扩展性问题以及认知适应性方面的差距等挑战依然存在。本研究强调了ITS和RTS的互补优势，提出了整合的混合解决方案以最大化教育效益。未来的研究应侧重于弥合可扩展性方面的差距、全面应对伦理考量，并推进AI模型以支持多样化的教育需求。