The Role of Generative AI in Facilitating Social Interactions: A Scoping Review

TL;DR

This paper reviews how generative AI technologies like LLMs and image tools are used to enhance social interactions, highlighting current applications, design approaches, and ethical considerations, based on analysis of 30 recent studies.

Contribution

It provides a comprehensive overview of GAI applications in social interaction, emphasizing participatory design and ethical issues, and identifies key trends and gaps in current research.

Findings

GAI applications span storytelling, socio-emotional training, reminiscence, and more.

Participatory and co-design approaches improve social engagement.

Ethical concerns include cultural bias and accessibility.

Abstract

Reduced social connectedness increasingly poses a threat to mental health, life expectancy, and general well-being. Generative AI (GAI) technologies, such as large language models (LLMs) and image generation tools, are increasingly integrated into applications aimed at enhancing human social experiences. Despite their growing presence, little is known about how these technologies influence social interactions. This scoping review investigates how GAI-based applications are currently designed to facilitate social interaction, what forms of social engagement they target, and which design and evaluation methodologies designers use to create and evaluate them. Through an analysis of 30 studies published since 2020, we identify key trends in application domains including storytelling, socio-emotional skills training, reminiscence, collaborative learning, music making, and general conversation. We highlight the role of participatory and co-design approaches in fostering both effective technology use and social engagement, while also examining socio-ethical concerns such as cultural bias and accessibility. This review underscores the potential of GAI to support dynamic and personalized interactions, but calls for greater attention to equitable design practices and inclusive evaluation strategies.