Intelligent tutoring systems (ITSs) that imitate human tutors and aim to provide immediate and customized instructions or feedback to learners have shown their effectiveness in education. With the emergence of generative artificial intelligence, large language models (LLMs) further entitle the systems to complex and coherent conversational interactions. These systems would be of great help in language education as it involves developing skills in communication, which, however, drew relatively less attention. Additionally, due to the complicated cognitive development at younger ages, more endeavors are needed for practical uses. Scaffolding refers to a teaching technique where teachers provide support and guidance to students for learning and developing new concepts or skills. It is an effective way to support diverse learning needs, goals, processes, and outcomes. In this work, we investigate how pedagogical instructions facilitate the scaffolding in ITSs, by conducting a case study on guiding children to describe images for language learning. We construct different types of scaffolding tutoring systems grounded in four fundamental learning theories: knowledge construction, inquiry-based learning, dialogic teaching, and zone of proximal development. For qualitative and quantitative analyses, we build and refine a seven-dimension rubric to evaluate the scaffolding process. In our experiment on GPT-4V, we observe that LLMs demonstrate strong potential to follow pedagogical instructions and achieve self-paced learning in different student groups. Moreover, we extend our evaluation framework from a manual to an automated approach, paving the way to benchmark various conversational tutoring systems.

翻译：智能辅导系统（ITSs）通过模仿人类教师，旨在为学习者提供即时且定制化的指导或反馈，已在教育领域展现出有效性。随着生成式人工智能的兴起，大语言模型（LLMs）进一步赋予这些系统开展复杂连贯对话交互的能力。此类系统在语言教育中大有裨益——因语言教育涉及沟通技能培养——然而相关研究却相对不足。此外，由于低龄学习者认知发展的复杂性，实际应用仍需更多探索。支架式教学是指教师为学生提供支持与引导以助其学习并发展新概念或技能的教学技术，是满足多样化学习需求、目标、过程与成果的有效途径。本研究通过开展引导儿童描述图像进行语言学习的案例研究，探究教学指令如何促进ITSs中的支架式教学。我们基于知识建构、探究式学习、对话教学与最近发展区四种基础学习理论，构建了不同类型的支架式辅导系统。为进行定性与定量分析，我们构建并完善了包含七个维度的评价量规以评估支架式教学过程。在针对GPT-4V的实验中，我们发现LLMs展现出遵循教学指令的强大潜力，并能针对不同学生群体实现自定步调学习。此外，我们将评估框架从人工评估拓展至自动化方法，为各类对话式辅导系统的基准测试奠定基础。