This research investigates distinct human-generative AI collaboration types
and students' interaction experiences when collaborating with generative AI
(i.e., ChatGPT) for problem-solving tasks and how these factors relate to
students' sense of agency and perceived collaborative problem solving. By
analyzing the surveys and reflections of 79 undergraduate students, we
identified three human-generative AI collaboration types: even contribution,
human leads, and AI leads. Notably, our study shows that 77.21% of students
perceived they led or had even contributed to collaborative problem-solving
when collaborating with ChatGPT. On the other hand, 15.19% of the human
participants indicated that the collaborations were led by ChatGPT, indicating
a potential tendency for students to rely on ChatGPT. Furthermore, 67.09% of
students perceived their interaction experiences with ChatGPT to be positive or
mixed. We also found a positive correlation between positive interaction
experience and a sense of positive agency. The results of this study contribute
to our understanding of the collaboration between students and generative AI
and highlight the need to study further why some students let ChatGPT lead
collaborative problem-solving and how to enhance their interaction experience
through curriculum and technology design.

研究了人机协作类型和学生与生成式 AI（即 ChatGPT）在解决问题任务时的互动体验，以及这些因素与学生的代理感和感知的协作问题解决之间的关系，通过分析 79 名本科生的调查和反思，发现了三种人机生成式 AI 协作类型：平等贡献、人类主导和 AI 主导。与此同时，本研究显示，77.21% 的学生感知在与 ChatGPT 协作时，他们主导或者有相等的贡献。另一方面，15.19% 的人类参与者表示合作由 ChatGPT 主导，表明一部分学生可能过于依赖 ChatGPT。此外，67.09% 的学生感知与 ChatGPT 的互动体验是积极或者融合了正面和负面的。研究发现积极的互动体验与积极代理感之间存在正相关。该研究结果增进了我们对学生和生成式 AI 之间协作的认识，并强调了进一步研究为何一些学生让 ChatGPT 主导协作问题解决，以及如何通过课程和技术设计提高他们的互动体验的需求。

人工智能协作解决问题中的人类生成者和互动方式学生认知

Human-Generative AI Collaborative Problem Solving Who Leads and How  Students Perceive the Interactions

During interaction with others, we perceive and produce social actions in
close temporal distance or even simultaneously. It has been argued that the
motor system is involved in perception and action, playing a fundamental role
in the handling of actions produced by oneself and by others. But how does it
distinguish in this processing between self and other, thus contributing to
self-other distinction? In this paper we propose a hierarchical model of
sensorimotor coordination based on principles of perception-action coupling and
predictive processing in which self-other distinction arises during action and
perception. For this we draw on mechanisms assumed for the integration of cues
for a sense of agency, i.e., the sense that an action is self-generated. We
report results from simulations of different scenarios, showing that the model
is not only able to minimize free energy during perception and action, but also
showing that the model can correctly attribute sense of agency to own actions.

本文提出了一个基于感知 - 动作耦合和预测处理原则的层级传感器运动协调模型，该模型能够在感知和动作过程中实现自我和他人的区分，并且具有正确地将代理感归因于自己动作的能力。