测试AI编程助手的三种互动方式，发现协作模式最有效。
Evaluating the Effectiveness of Large Language Models in Solving Simple Programming Tasks: A User-Centered Study
- 设计三种AI交互模式：被动响应、主动建议、双向协作。
- 协作模式使高中生完成编程任务时间显著缩短。
- 适合教育场景中提升初学者学习体验的AI设计参考。
随着大语言模型（LLMs）在教育工具和编程环境中的普及，其与用户的交互方式成为关键问题。本研究通过一个包含15名高中生的自身对照实验，考察了ChatGPT-4o在三种不同交互风格（被动、主动、协作）下对简单编程任务表现的影响。每种风格对应特定支持模式：仅在提问时回应、自动提供建议，或与用户进行来回对话。定量分析显示，协作模式显著提升了任务完成速度，参与者也报告更高的满意度和感知帮助度。结果表明，大语言模型的沟通方式、引导策略与响应机制会显著影响学习成效与用户体验。该研究强调，针对初学者应设计更具交互性、适应性和以用户为中心的AI系统，而不仅是追求功能正确。
原文摘要 · Abstract (English)
As large language models (LLMs) become more common in educational tools and programming environments, questions arise about how these systems should interact with users. This study investigates how different interaction styles with ChatGPT-4o (passive, proactive, and collaborative) affect user performance on simple programming tasks. I conducted a within-subjects experiment where fifteen high school students participated, completing three problems under three distinct versions of the model. Each version was designed to represent a specific style of AI support: responding only when asked, offering suggestions automatically, or engaging the user in back-and-forth dialogue.Quantitative analysis revealed that the collaborative interaction style significantly improved task completion time compared to the passive and proactive conditions. Participants also reported higher satisfaction and perceived helpfulness when working with the collaborative version. These findings suggest that the way an LLM communicates, how it guides, prompts, and responds, can meaningfully impact learning and performance. This research highlights the importance of designing LLMs that go beyond functional correctness to support more interactive, adaptive, and user-centered experiences, especially for novice programmers.
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