Measuring machine companionship experiences: Scale development and validation for AI companions

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.