通过辅导系统推进教育：一项系统性文献综述
Advancing Education through Tutoring Systems: A Systematic Literature Review
AI总结：
本文基于PRISMA框架系统综述86项研究，分析智能辅导系统与机器人辅导系统的技术进展、参与策略和伦理问题，识别出三类系统并提出整合式混合方案以提升教育效果。
摘要：
本研究系统性地回顾了辅导系统在应对全球教育挑战中的变革性作用，涵盖智能辅导系统（ITS）和机器人辅导系统（RTS），这些系统借助先进技术来应对教育难题。由于许多学生在核心学科领域的能力掌握上存在困难，辅导系统作为有前景的解决方案应运而生，通过提供个性化和自适应教学来弥合学习差距。ITS利用人工智能（AI）模型，如贝叶斯知识追踪和大语言模型，提供精准的认知支持，而RTS则通过类人交互增强社交和情感参与。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，进行了潜在类别分析，识别出三个不同类别：基于计算机的ITS、基于机器人的RTS，以及整合多种交互模式的多模态系统。研究结果揭示了AI技术在增强适应性、参与度和学习成果方面的显著进展。然而，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仍然存在。本研究强调了ITS和RTS的互补优势，提出整合式混合解决方案以最大化教育效益。未来研究应侧重于弥合可扩展性方面的差距、全面解决伦理考量，并推进AI模型以支持多样化的教育需求。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
