Generative AI as a social partner for young adults: Dual pathways to resilience and AI dependence

This study examines LLM-based AI chatbots as social interaction partners. It identifies young adults’ support-seeking motivations and models their associations with two interaction processes (Perceived Authenticity and Interaction Intensity) and two psychological outcomes (Resilience and AI Dependence). In Study 1, we developed the Motivation Scale for Generative AI (MS-GAI) through theory-driven item generation and interviews and validated it with a survey of Korean young adults (N = 300). Exploratory and confirmatory factor analyses supported an eight-factor, 40-item structure grounded in four social-support domains and supplemented by context-specific motivations. In Study 2, structural equation modeling showed that Emotional Support and Self-Enhancement were positively associated with both Perceived Authenticity and Interaction Intensity. Objective Advice Seeking was positively associated with Perceived Authenticity, whereas Learning Assistance, Daily Problem-Solving, and Entertainment/Mood Diversion were positively associated with Interaction Intensity. Perceived Authenticity was also positively associated with Interaction Intensity. Both interaction processes were positively related to self-reported Resilience and AI Dependence, with Interaction Intensity showing the stronger association with AI Dependence. These findings identify distinct but overlapping interaction pathways through which support-seeking motivations are associated with adaptive and maladaptive outcomes. The study extends understanding of social support in human–AI interaction by providing a validated motivation measure and showing how shared interaction processes relate to both short-term perceived recovery and dependence. The findings are consistent with the Paradoxes of Technology perspective and highlight the need for emotionally supportive yet balanced design and policy safeguards.