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

2026-02-09

AI Summary

Existing research on human-AI collaborative learning predominantly relies on static or post-hoc evaluations, which struggle to capture the dynamic evolution of inclusivity. This work proposes the first interaction-process-centered analytical framework for inclusivity, operationalizing it through three dimensions—participatory equity, affective climate, and cognitive equity—using fine-grained, discourse-level interaction data to construct quantifiable and scalable dynamic metrics. Through simulated dialogues and human-AI collaboration experiments, the framework effectively uncovers latent mechanisms such as participation patterns, relational dynamics, and perspective uptake. By foregrounding the temporal and interactive nature of collaboration, this study establishes a novel, process-oriented paradigm for measuring inclusivity in human-AI collaborative learning environments.

Technology Category

Humans and AI: Learning Human Values and PreferencesCognitive Modeling & Cognitive Systems: Social Cognition And InteractionApplication Domains: Humanities & Computational Social Science

Application Category

Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systemsSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• inclusion
• collaborative problem solving
• human-AI collaboration
• equity
• interactional dynamics

Innovation

• inclusion analytics
• human-AI collaboration
• participation equity
• epistemic equity
• interaction-level measurement