Recently, research into chatbots (also known as conversational agents, artificial intelligence agents, or voice assistants), which are computer applications using artificial intelligence to mimic human-like conversation, has grown sharply. Despite this growth, sociology lags behind other disciplines (including computer science, medicine, psychology, and communication) in publishing about chatbots. We suggest sociology can advance the understanding of human-chatbot interaction and offer 4 sociological theories to enhance extant work in this field. The first 2 theories (resource substitution theory and power-dependence theory) add new insights to existing models of the drivers of chatbot use, which overlook sociological concerns about how social structure (eg, systemic discrimination and the uneven distribution of resources within networks) inclines individuals to use chatbots, including problematic levels of emotional dependency on chatbots. The second 2 theories (affect control theory and fundamental cause of disease theory) help inform the development of chatbot-driven interventions that minimize safety risks by integrating a sociologically informed normative framework (eg, affective norms) into chatbot alignment and enhance equity by enhancing access to community resources (eg, opportunities for civic participation). We discuss how the theories advance theorizing about human-chatbot interaction and developing chatbots for social good, which are chatbots that provide scalable solutions to social and environmental challenges facing humanity while supporting human agency.
Campos-Castillo et al. (2026) studied this question.