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

Collaborative problem solving (CPS) in teams is tightly coupled with the
creation of shared meaning between participants in a situated, collaborative
task. In this work, we assess the quality of different utterance segmentation
techniques as an aid in annotating CPS. We (1) manually transcribe utterances
in a dataset of triads collaboratively solving a problem involving dialogue and
physical object manipulation, (2) annotate collaborative moves according to
these gold-standard transcripts, and then (3) apply these annotations to
utterances that have been automatically segmented using toolkits from Google
and OpenAI's Whisper. We show that the oracle utterances have minimal
correspondence to automatically segmented speech, and that automatically
segmented speech using different segmentation methods is also inconsistent. We
also show that annotating automatically segmented speech has distinct
implications compared with annotating oracle utterances--since most annotation
schemes are designed for oracle cases, when annotating automatically-segmented
utterances, annotators must invoke other information to make arbitrary
judgments which other annotators may not replicate. We conclude with a
discussion of how future annotation specs can account for these needs.

评估不同话语分割技术在协作问题解决注释中的有效性，自动分割和手动划分有所不同，且自动划分中的标注需要调用其他信息做出主观判断。

自动分割作为多模式话语注释辅助工具的效果如何？

How Good is Automatic Segmentation as a Multimodal Discourse Annotation  Aid?

Collaborative problem solving (CPS) enables student groups to complete
learning tasks, construct knowledge, and solve problems. Previous research has
argued the importance to examine the complexity of CPS, including its
multimodality, dynamics, and synergy from the complex adaptive systems
perspective. However, there is limited empirical research examining the
adaptive and temporal characteristics of CPS which might lead to an
oversimplified representation of the real complexity of the CPS process. To
further understand the nature of CPS in online interaction settings, this
research collected multimodal process and performance data (i.e., verbal
audios, computer screen recordings, concept map data) and proposed a
three-layered analytical framework that integrated AI algorithms with learning
analytics to analyze the regularity of groups collaboration patterns. The
results detected three types of collaborative patterns in groups, namely the
behaviour-oriented collaborative pattern (Type 1) associated with medium-level
performance, the communication - behaviour - synergistic collaborative pattern
(Type 2) associated with high-level performance, and the communication-oriented
collaborative pattern (Type 3) associated with low-level performance. The
research further highlighted the multimodal, dynamic, and synergistic
characteristics of groups collaborative patterns to explain the emergence of an
adaptive, self-organizing system during the CPS process.

本研究通过收集多模态过程和表现数据并将人工智能算法与学习分析相结合，提出了一个三层分析框架来分析团队协作模式的规律。结果检测到三种协作模式：中等水平的基于行为的协作模式（Type 1），与高水平表现相关的基于沟通 - 行为 - 协同模式（Type 2）以及与低水平表现相关的基于沟通的协作模式（Type 3）。研究进一步强调了团队协作模式的多模态，动态和协同特性，以解释在 CPS 过程中出现的自适应，自组织系统的出现。

基于人工智能的学习分析方法，从复杂自适应系统的角度考察协作解决问题的过程

An Artificial Intelligence driven Learning Analytics Method to Examine the Collaborative Problem solving Process from a Complex Adaptive Systems Perspective