Feeling included by AI: belonging and authenticity in human–AI interaction

Purpose This study aims to examine how users’ experiences of artificial intelligence (AI) inclusion, reflected in feelings of belonging and authenticity during human–AI interaction, are associated with user satisfaction and interactional justice. Design/methodology/approach The study assesses the proposed relationships between the constructs using two separately analyzed data sets: 219 Amazon Alexa users and 577 ChatGPT users. Regression analysis was used to assess the proposed relationships within each data set. Additionally, the study adopts Latent Profile Analysis (LPA) to identify distinct AI user profiles. Findings The findings indicate that AI inclusion dimensions (i.e. belonging and authenticity) are positively associated with user satisfaction and interactional justice. The LPA results reveal different user profiles based on inclusion, satisfaction and justice scores. Research limitations/implications While this study assesses AI inclusion in the contexts of Amazon Alexa and ChatGPT, future research may explore AI inclusion in other AI systems and use longitudinal or experimental methodologies. Previous research has largely approached AI inclusion through access, participation or design-oriented perspectives, whereas this study examines AI inclusion from a micro-level experiential perspective. Practical implications The findings of this study have several implications for AI developers, organizations, policymakers, society and education by showing that AI systems should be designed not only for functional performance but also to foster users’ feelings of belonging and authenticity. Hence, users’ emotional experiences should be treated as an important dimension of AI evaluation and governance. Originality/value This study advances research on AI and inclusion by conceptualizing AI inclusion as users’ felt experience during human–AI interactions rather than access to, participation in, or governance of AI systems. It defines AI inclusion as a micro-level experiential construct and highlights its importance for understanding user satisfaction and interactional justice.