Measuring Inclusion in Interaction: Inclusion Analytics for Human-AI Collaborative Learning

• URL: http://arxiv.org/abs/2602.09269v1
• Date: Mon, 09 Feb 2026 23:07:15 GMT
• Title: Measuring Inclusion in Interaction: Inclusion Analytics for Human-AI Collaborative Learning
• Authors: Jaeyoon Choi, Nia Nixon,
• Abstract summary: We introduce inclusion analytics, a discourse-based framework for examining inclusion as a dynamic, interactional process in problem solving.<n>We demonstrate how these constructs can be made analytically visible using scalable, interaction-level measures.<n>This work represents an initial step toward process-oriented approaches to measuring inclusion in human-AI collaborative learning environments.
• Score: 1.0742675209112622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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