In order to train children's ability to ask curiosity-driven questions, previous research has explored designing specific exercises relying on providing semantic and linguistic cues to help formulate such questions. But despite showing pedagogical efficiency, this method is still limited as it relies on generating the said cues by hand, which can be a very costly process. In this context, we propose to leverage advances in the natural language processing field (NLP) and investigate the efficiency of using a large language model (LLM) for automating the production of the pedagogical content of a curious question-asking (QA) training. We study generating the said content using the "prompt-based" method that consists of explaining the task to the LLM in natural text. We evaluate the output using human experts annotations and comparisons with hand-generated content. Results suggested indeed the relevance and usefulness of this content. We also conduct a field study in primary school (75 children aged 9-10), where we evaluate children's QA performance when having this training. We compare 3 types of content : 1) hand-generated content that proposes "closed" cues leading to predefined questions; 2) GPT-3-generated content that proposes the same type of cues; 3) GPT-3-generated content that proposes "open" cues leading to several possible questions. We see a similar QA performance between the two "closed" trainings (showing the scalability of the approach using GPT-3), and a better one for participants with the "open" training. These results suggest the efficiency of using LLMs to support children in generating more curious questions, using a natural language prompting approach that affords usability by teachers and other users not specialists of AI techniques. Furthermore, results also show that open-ended content may be more suitable for training curious question-asking skills.

翻译：为训练儿童提出好奇心驱动问题的能力，以往研究探索了通过提供语义与语言线索来设计特定练习，以帮助其构建此类问题。尽管该方法展现了教学有效性，但其局限性在于需要人工生成上述线索，这一过程成本高昂。在此背景下，我们提出利用自然语言处理领域的进展，探究通过大型语言模型自动化生成好奇心提问训练教学内容的效率。本研究采用基于"提示"的方法生成此类内容，即用自然语言向LLM解释任务。我们通过人类专家标注及与人工生成内容的对比评估输出结果，结果显示该内容具有显著相关性与实用性。进一步地，我们在小学（75名9-10岁儿童）开展实地研究，评估儿童在接收训练后的提问表现。我们比较三种内容类型：1）人工生成的"封闭式"线索（导向预设问题）；2）GPT-3生成的同类线索；3）GPT-3生成的"开放式"线索（导向多种可能问题）。两种"封闭式"训练下的提问表现相近（验证了GPT-3方法的可扩展性），而接受"开放式"训练的参与者表现更优。这些结果表明，通过自然语言提示方法（教师及非AI技术专业用户可操作），利用LLM支撑儿童提出更多好奇心驱动问题是有效的。此外，结果提示开放式内容可能更适用于训练好奇心提问能力。