Why Socially Anxious Young Adults Turn to Chatbots: New Study Maps the Path to Problematic AI Use

For millions of young people, conversational AI chatbots have quietly become part of daily life — tools to ask questions, draft messages, and, increasingly, to talk to when no human is available. A new study from China suggests that for socially anxious young adults, these systems may fill a psychological role that goes far beyond convenience, and that the road from social discomfort to problematic AI use runs through three distinct psychological mechanisms: seeing the machine as human, becoming emotionally attached to it, and losing the executive control needed to step away.

The research, led by Yuanyuan Chen of Shaanxi University of Technology and Yongquan Huo of Shaanxi Normal University, together with colleagues at Anhui University of Chinese Medicine, Xi’an University, Tianshui Normal University, and Shaanxi University of Technology, was published in the International Journal of Mental Health and Addiction. The team recruited 1,034 participants aged 18 to 26 years from two universities in Xi’an and Haikou, China, and asked them to complete a detailed questionnaire measuring social anxiety, perceptions of their AI chatbots, emotional bonds with the systems, executive-function difficulties, and patterns of problematic use of conversational AI, abbreviated PUCAI in the study.

The headline finding is stark: social anxiety positively predicted problematic use of conversational AI. In other words, the more socially anxious a young adult reported being, the more likely they were to show signs of problematic engagement with chatbots — patterns resembling the loss of control, preoccupation, and continued use despite negative consequences that researchers associate with behavioral addictions. The result echoes earlier work, including a 2023 study in Computers in Human Behavior by Hu, Mao, and Kim, which found that loneliness, rumination, and mind perception linked social anxiety to problematic chatbot use, and a 2023 study by Wei and colleagues showing that adolescents’ social anxiety can trigger problematic smartphone use.

But the new study goes further by mapping the internal machinery of that relationship. Using statistical mediation analysis, the researchers found that three variables operated in parallel between social anxiety and PUCAI: perceived anthropomorphism, emotional attachment, and executive-function difficulties. Perceived anthropomorphism refers to the tendency to attribute human-like mental states, intentions, and personality to non-human agents — a phenomenon famously theorized by Epley, Waytz, and Cacioppo in their 2007 three-factor theory of anthropomorphism. When socially anxious users perceive a chatbot as more human-like, the machine becomes a safer social partner than any person, one that never judges, interrupts, or walks away.

Emotional attachment, the second mediator, describes the affective bond users develop with the system — a construct borrowed from consumer psychology, where Park and colleagues distinguished attachment from mere attitude toward a brand. In the context of conversational AI, attachment means the chatbot becomes a source of comfort and connection, something the user turns to in distress. Qualitative research on the social chatbot Replika, including a 2024 grounded theory analysis by Laestadius and colleagues published in New Media & Society, has documented how emotional dependence on such systems can produce real psychological harms — users described the bots as simultaneously too human and not human enough, a paradox that left them feeling both connected and bereft.

The third mediator, executive-function difficulties, brings the brain’s cognitive control system into the picture. Executive functions — working memory, inhibition, and cognitive flexibility — are orchestrated largely by prefrontal cortical networks, as Menon and D’Esposito outlined in Neuropsychopharmacology in 2022. These capacities mature gradually from adolescence into adulthood, following what Tervo-Clemmens and colleagues described in Nature Communications in 2023 as a canonical trajectory of executive function maturation. Young adults aged 18 to 26 sit squarely within this window of ongoing maturation, which may make them particularly vulnerable when the pull of an always-available, endlessly patient conversational partner collides with still-developing self-regulation. The study found that social anxiety predicted greater executive-function difficulties, which in turn predicted more problematic AI use — consistent with the I-PACE model’s insistence that addiction-like behaviors emerge from the interaction of personal predispositions, affective states, cognitive processes, and execution deficits.

That theoretical framing is central to the paper. The Interaction of Person-Affect-Cognition-Execution model, developed by Matthias Brand and colleagues and updated in Neuroscience and Biobehavioral Reviews in 2019, holds that addictive behaviors are not simply responses to a substance or platform but the product of a person’s vulnerabilities meeting affective and cognitive processes that gradually narrow and intensify until they dominate behavior. The new findings fit this architecture precisely: social anxiety supplies the person-level vulnerability, anthropomorphism and attachment supply the affective-cognitive engine, and executive-function difficulties supply the execution failure that turns heavy use into uncontrolled use.

The study also drew on Conservation of Resources theory, proposed by Stevan Hobfoll and elaborated by Shelley Taylor and colleagues, which holds that psychological well-being depends on maintaining valued internal and external resources — self-esteem, optimism, social support, a sense of mastery. Stress arises when those resources are threatened or lost. From this perspective, socially anxious individuals enter interactions with a depleted resource pool, and a chatbot that demands no social resources at all becomes an attractive refuge. Crucially, the researchers found that psychosocial resources moderated two of the key pathways: the associations of social anxiety with perceived anthropomorphism and with emotional attachment. In participants with richer psychosocial resources — greater perceived social support, psychological capital, and resilience — the link between social anxiety and seeing the chatbot as human, and the link between social anxiety and becoming attached to it, were weaker. Resources, in other words, act as a psychological buffer, dampening the tendency of social anxiety to convert into machine-directed intimacy.

This moderating effect carries real weight beyond theory. Taylor and colleagues have shown, in neuroimaging work published in the Journal of Personality and Social Psychology in 2008, that psychosocial resources literally moderate the brain’s cortisol stress responses, engaging prefrontal circuitry that down-regulates threat systems. Applied to the chatbot context, the message is that the same social anxiety can lead down very different paths depending on the resources a person carries: those with strong support networks and psychological reserves may use AI lightly and instrumentally, while those without may slide toward anthropomorphizing, attaching, and losing control. The findings also align with Chinese research on left-behind children by Fan and colleagues, which showed that psychosocial resources buffer the effects of adversity on social adjustment through similar moderated-mediation logic.

The practical implications are immediate. The authors suggest that interventions aimed at reducing problematic conversational AI use among socially anxious young adults should not simply target screen time. Instead, they should build psychosocial resources — social support, coping skills, psychological capital — and strengthen executive functions, while also addressing the anthropomorphism and attachment processes that make chatbots feel like companions. The timing could hardly be more urgent: China’s Internet Network Information Center reported in its 57th statistical survey that hundreds of millions of Chinese users now navigate an internet saturated with generative AI, and chatbots such as ChatGPT, Replika, and their Chinese counterparts are reaching ever younger audiences. At the same time, the field remains contested — researchers such as Ciudad-Fernández, von Hammerstein, and Billieux have argued in Addictive Behaviors that people are not becoming AIholic and that the ChatGPT addiction construct needs scrutiny. This study does not claim that chatbots are inherently addictive; it claims something more nuanced — that for a specific, identifiable group of vulnerable users, particular psychological pathways make problematic use likely, and that those pathways can be measured, understood, and interrupted. As conversational AI becomes the default interface between young people and the digital world, understanding who is at risk, and why, may prove one of the most consequential questions in mental health research.

Subject of Research: Psychology

Article Title: Social Anxiety and Problematic Use of Conversational AI: I-PACE Model and Conservation of Resources Perspectives

Article References: Social Anxiety and Problematic Use of Conversational AI: I-PACE Model and Conservation of Resources Perspectives. (n.d.). https://doi.org/10.1007/s11469-026-01750-z

Image Credits: AI Generated

DOI: 10.1007/s11469-026-01750-z

Keywords: social anxiety, conversational AI, chatbots, problematic AI use, I-PACE model, conservation of resources, executive function, emotional attachment, anthropomorphism, mental health, Social, Anxiety