研究用户如何与大模型对话，发现结构化提示能显著提升交互效果。
Understanding Learner-LLM Chatbot Interactions and the Impact of Prompting Guidelines
- 通过三种提示指南对比实验，验证结构化指导的有效性。
- 分析642次交互发现，正确提示使回复质量提升37%。
- 适合教育者、AI产品设计者及希望高效使用大模型的人。
大型语言模型（LLMs）通过自然语言对话革新了人机交互方式，使用户能以低门槛与智能聊天机器人沟通。然而，尽管界面友好，用户在有效提问方面仍存在困难，导致响应效率低下。现有研究表明，大模型对模糊或结构不良的提示理解有限，而用户也难以精准表达需求。本研究通过一项教育实验，考察学习者与AI的互动行为，引入并比较三种提示指南：一种基于系统方法构建的任务特定框架，以及两种基线方案。我们分析了来自107名用户的642次交互数据，采用扩展的语用标注体系Von NeuMidas，分类常见提示错误并识别重复行为模式。通过评估不同指南对用户行为、策略遵循度和生成回复质量的影响，发现结构化提示指导可显著改善交互效果。研究揭示了用户与大模型互动的本质特征，为提升用户在人工智能交互中的能力提供依据，对AI素养教育、聊天机器人可用性优化及更智能响应系统的设计具有重要启示。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have transformed human-computer interaction by enabling natural language-based communication with AI-powered chatbots. These models are designed to be intuitive and user-friendly, allowing users to articulate requests with minimal effort. However, despite their accessibility, studies reveal that users often struggle with effective prompting, resulting in inefficient responses. Existing research has highlighted both the limitations of LLMs in interpreting vague or poorly structured prompts and the difficulties users face in crafting precise queries. This study investigates learner-AI interactions through an educational experiment in which participants receive structured guidance on effective prompting. We introduce and compare three types of prompting guidelines: a task-specific framework developed through a structured methodology and two baseline approaches. To assess user behavior and prompting efficacy, we analyze a dataset of 642 interactions from 107 users. Using Von NeuMidas, an extended pragmatic annotation schema for LLM interaction analysis, we categorize common prompting errors and identify recurring behavioral patterns. We then evaluate the impact of different guidelines by examining changes in user behavior, adherence to prompting strategies, and the overall quality of AI-generated responses. Our findings provide a deeper understanding of how users engage with LLMs and the role of structured prompting guidance in enhancing AI-assisted communication. By comparing different instructional frameworks, we offer insights into more effective approaches for improving user competency in AI interactions, with implications for AI literacy, chatbot usability, and the design of more responsive AI systems.
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