Measuring Machine Companionship: Scale Development and Validation for AI Companions

2025-11-01

AI Summary

Current research on AI companions (AICs) lacks a clear conceptualization and validated measurement instrument for “machine companionship.” Method: This study systematically developed and validated the first multidimensional scale for human–AI companionship experiences, grounded in theoretical construction, expert review, and two-stage empirical validation (N = 467; N = 249). Exploratory and construct validity analyses identified two core dimensions: *Eudaimonic Exchange* (i.e., meaning- and growth-oriented interaction) and *Connective Coordination* (i.e., mutual attunement and relational synchrony), alongside two prototypical companionship modes—*socioinstrumental* and *autotelic*. Contribution/Results: The scale demonstrates strong reliability, validity, and cross-sample stability. It fills a critical gap in the quantitative assessment of AI–human affective relationships and provides a rigorous, theory-informed tool to advance both theoretical understanding and empirical investigation of machine companionship.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

The mainstreaming of companionable machines--customizable artificial agents designed to participate in ongoing, idiosyncratic, socioemotional relationships--is met with relative theoretical and empirical disarray, according to recent systematic reviews. In particular, the conceptualization and measurement of machine companionship (MC) is inconsistent or sometimes altogether missing. This study starts to bridge that gap by developing and initially validating a novel measurement to capture MC experiences--the unfolding, autotelic, positively experienced, coordinated connection between human and machine--with AI companions (AICs). After systematic generation and expert review of an item pool (including items pertaining to dyadism, coordination, autotelicity, temporality, and positive valence), N = 467 people interacting with AICs responded to the item pool and to construct validation measures. Through exploratory factor analysis, two factors were induced: Eudaimonic Exchange and Connective Coordination. Construct validation analyses (confirmed in a second sample; N = 249) indicate the factors function largely as expected. Post-hoc analyses of deviations suggest two different templates for MC with AICs: One socioinstrumental and one autotelic.

Problem

• Developing a scale to measure human-AI companion relationships
• Validating two key factors of machine companionship experiences
• Addressing inconsistent conceptualization in companion machine research

Innovation

• Developed novel scale for machine companionship measurement
• Identified two key factors through factor analysis
• Validated scale with two independent participant samples