Educational chatbots come with a promise of interactive and personalized learning experiences, yet their development has been limited by the restricted free interaction capabilities of available platforms and the difficulty of encoding knowledge in a suitable format. Recent advances in language learning models with zero-shot learning capabilities, such as ChatGPT, suggest a new possibility for developing educational chatbots using a prompt-based approach. We present a case study with a simple system that enables mixed-turn chatbot interactions and discuss the insights and preliminary guidelines obtained from initial tests. We examine ChatGPT's ability to pursue multiple interconnected learning objectives, adapt the educational activity to users' characteristics, such as culture, age, and level of education, and its ability to use diverse educational strategies and conversational styles. Although the results are encouraging, challenges are posed by the limited history maintained for the conversation and the highly structured form of responses by ChatGPT, as well as their variability, which can lead to an unexpected switch of the chatbot's role from a teacher to a therapist. We provide some initial guidelines to address these issues and to facilitate the development of effective educational chatbots.

翻译：教育聊天机器人承诺提供互动性和个性化学习体验，但其发展一直受限于现有平台有限的自由交互能力以及以合适形式编码知识的难度。近年来，具备零样本学习能力的大语言模型（如ChatGPT）的进步，为采用基于提示的方法开发教育聊天机器人提供了新可能。我们通过一个支持混合轮次对话互动的简易系统开展案例研究，并讨论从初步测试中获得的洞见与初步指南。我们考察了ChatGPT在实现多个相互关联的学习目标、根据用户特征（如文化背景、年龄及教育水平）调整教育活动、以及运用多样化教育策略与对话风格方面的能力。尽管结果令人鼓舞，但挑战依然存在：对话历史记录有限、ChatGPT回复的高度结构化形式及其变异性——这可能导致聊天机器人角色意外从教师转变为治疗师。我们提供了一些初步指南来解决这些问题，并促进高效教育聊天机器人的开发。