用对话分析法实时捕捉人机协作中的包容性动态
Measuring Inclusion in Interaction: Inclusion Analytics for Human-AI Collaborative Learning
- 从参与公平、情感氛围、认知平等三维度构建包容性分析框架
- 通过对话级指标揭示传统统计无法发现的互动模式
- 适合关注人机协作公平性的教育AI研究者使用
包容性、公平性和可及性在人工智能与教育领域备受重视，但通常依赖粗略的样本描述或事后自评，难以捕捉协作解决问题（CPS）过程中包容性如何实时演变。本文提出包容性分析（inclusion analytics），一种基于话语的框架，将包容性视为协作过程中的动态交互现象。我们从三个互补维度定义包容性：参与公平性、情感氛围和认知平等，并展示如何通过可扩展的对话层级度量使其可分析化。结合模拟对话与真实人机协作实验数据，证明该方法能揭示参与模式、关系动态和观点采纳等传统聚合或事后评估无法察觉的规律。本工作为测量人机协作学习环境中包容性的过程导向方法迈出初步一步。
原文摘要 · Abstract (English)
Inclusion, equity, and access are widely valued in AI and education, yet are often assessed through coarse sample descriptors or post-hoc self-reports that miss how inclusion is shaped moment by moment in collaborative problem solving (CPS). In this proof-of-concept paper, we introduce inclusion analytics, a discourse-based framework for examining inclusion as a dynamic, interactional process in CPS. We conceptualize inclusion along three complementary dimensions -- participation equity, affective climate, and epistemic equity -- and demonstrate how these constructs can be made analytically visible using scalable, interaction-level measures. Using both simulated conversations and empirical data from human-AI teaming experiments, we illustrate how inclusion analytics can surface patterns of participation, relational dynamics, and idea uptake that remain invisible to aggregate or post-hoc evaluations. This work represents an initial step toward process-oriented approaches to measuring inclusion in human-AI collaborative learning environments.
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