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：为培养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训练方案。尽管该方法展现出教学有效性，但因其依赖人工生成线索成本高昂而存在局限性。在此背景下，我们提出利用自然语言处理领域最新进展，探究使用大型语言模型自动化生成好奇提问训练教学内容的效率。本研究采用基于提示的方法——即通过自然语言文本向LLM描述任务——来生成教学内容，并通过专家人工标注与手工生成内容进行对比评估。结果表明生成内容具有显著相关性与实用性。我们还在小学开展实地研究（75名9-10岁儿童），评估三种教学内容对儿童提问表现的影响：1）手工生成的封闭式线索（导向预设问题）；2）GPT-3生成的同类型封闭式线索；3）GPT-3生成的开放式线索（导向多种可能问题）。结果显示，两种封闭式训练使儿童提问表现相当（证明基于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，采用自然语言提示方法（便于教师等非AI技术专家使用）利用LLM支持儿童提出更多好奇性问题具有有效性，同时开放式内容可能更适合培养好奇提问技能。