发表机构
University of Florida; University of Pennsylvania; North Carolina State University; The University of Tennessee（佛罗里达大学; 宾夕法尼亚大学; 北卡罗来纳州立大学; 田纳西大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究以有序网络分析方法，考察中学生协作开发AI聊天机器人的互动，刻画协作随时间的组织及互动模式与机器人质量、AI知识成果的关系，揭示高质量机器人与特定序列及互动模式有关，为协作式AI开发提供过程描述并拓展了相关研究。
AI 中文摘要
随着人工智能（AI）教育成为K-12课程的关键组成部分，诸如设计和开发对话代理的活动越来越多地被用作教学实践。先前的工作主要通过关注学生的学习成果或最终人工智能产品的质量来研究这些活动，对人工智能系统开发过程中学习展开的协作过程洞察有限。尽管人工智能教育社区在研究STEM和计算机教育中的协作学习方面有着悠久的历史，但学生构建人工智能系统的人工智能学习环境的出现，为理解人工智能教育背景下的协作如何展开提供了新机会。基于这些基础工作，本研究考察了参与人工智能聊天机器人设计和开发的中学生之间的协作互动。通过对学生对话和开发行动进行有序网络分析，我们刻画了协作如何随时间组织，以及互动模式如何与聊天机器人质量和人工智能知识成果相关。结果表明，高质量的聊天机器人与连接解释、测试和完善的更整合序列相关。涉及清晰推理以及根据聊天机器人输出进行重复测试和修订的互动模式，也与更强的人工智能知识成果相关。这些发现提供了一个面向过程的协作式人工智能聊天机器人开发描述，并将人工智能教育社区关于协作学习过程的研究扩展到了人工智能教育背景中。
英文摘要
As Artificial Intelligence (AI) education has become a key component of K-12 curricula, activities such as designing and developing conversational agents are increasingly used as instructional practice. Prior work has primarily examined these activities by focusing on students' learning outcomes or the quality of final AI artifacts, offering limited insight into the collaborative processes through which learning unfolds during AI system development. Although the AIED community has a long history of studying collaborative learning in STEM and Computing education, the emergence of AI learning environments in which students build AI systems presents new opportunities to understand how collaboration unfolds in AI education contexts. Grounded in these foundational works, the current study examines collaborative interaction among middle school students engaged in the design and development of an AI chatbot. Using Ordered Network Analysis of students' dialogue and development actions, we characterize how collaboration is organized over time and how interaction patterns relate to chatbot quality and AI knowledge outcomes. Results reveal that higher-quality chatbots are associated with more integrated sequences linking explanation, testing, and refinement. Interaction patterns involving articulated reasoning and repeated testing and revision in response to chatbot output were also associated with stronger AI knowledge outcomes. These findings provide a process-oriented account of collaborative AI chatbot development and extend AIED research on collaborative learning processes to AI education contexts.
