The emergence of generative AI has accelerated the development of conversational tutoring systems that interact with students through natural language dialogue. Unlike prior intelligent tutoring systems (ITS), which largely function as adaptive and interactive problem sets with feedback and hints, conversational tutors hold the potential to simulate high-quality human tutoring by engaging with students' thoughts, questions, and misconceptions in real time. While some previous ITS, such as AutoTutor, could respond conversationally, they were expensive to author and lacked a full range of conversational ability. Generative AI has changed the capacity of ITS to engage conversationally. However, realizing the full potential of conversational tutors requires careful consideration of what research on human tutoring and ITS has already established, while also unpacking what new research will be needed. This paper synthesizes tenets of successful human tutoring, lessons learned from legacy ITS, and emerging work on conversational AI tutors. We use a keep, change, center, study framework for guiding the design of conversational tutoring. We argue that systems should keep proven methods from prior ITS, such as knowledge tracing and affect detection; change how tutoring is delivered by leveraging generative AI for dynamic content generation and dialogic scaffolding; and center opportunities for meaning-making, student agency, and granular diagnosis of reasoning. Finally, we identify areas requiring further study, including efficacy testing, student experience, and integration with human instruction. By synthesizing insights from human tutoring, legacy ITS, and emerging generative AI technologies, this paper outlines a research agenda for developing conversational tutors that are scalable, pedagogically effective, and responsive to the social and motivational dimensions of learning.

翻译：生成式人工智能的出现加速了对话式辅导系统的发展，这类系统通过自然语言对话与学生互动。与先前主要作为具有反馈和提示的自适应交互式问题集的智能辅导系统不同，对话式导师有潜力通过实时参与学生的思考、提问和误解来模拟高质量的人类辅导。虽然先前的一些智能辅导系统（如AutoTutor）能够进行对话式响应，但其构建成本高昂且缺乏全面的对话能力。生成式AI改变了智能辅导系统进行对话式互动的能力。然而，要充分实现对话式导师的潜力，需要仔细考量人类辅导和智能辅导系统研究已取得的成果，同时明确哪些新研究是必要的。本文综合了成功人类辅导的原则、传统智能辅导系统的经验教训以及对话式AI导师的新兴研究成果。我们采用"保留、改变、聚焦、研究"框架来指导对话式辅导的设计。我们认为系统应当保留先前智能辅导系统中已验证有效的方法，如知识追踪和情感检测；通过利用生成式AI进行动态内容生成和对话式支架来改变辅导的交付方式；并聚焦于意义建构、学生能动性以及精细化推理诊断的机会。最后，我们确定了需要进一步研究的领域，包括效能测试、学生体验以及与人类教学的整合。通过综合人类辅导、传统智能辅导系统和新兴生成式AI技术的洞见，本文勾勒了一个研究议程，旨在开发具有可扩展性、教学有效性并能响应学习的社会与动机维度的对话式导师。