Problematic Reliance on Generative AI in an Anxious Young Adult: A Case Report

The report proposes that generative AI can become a new target for reassurance and validation in anxious patients, with use spanning professional and personal contexts and reflecting a generalized strategy for managing uncertainty and self-doubt rather than task-specific assistance. Whereas prior discussion often emphasizes the cognitive-support benefits of AI, this report links excessive reliance to mechanisms that may perpetuate anxiety and presents this behavioral pattern in a clinical case. A single case report based on clinical interview and observation; the text notes that psychiatric consultation did not suggest a personality disorder and that clinically significant perfectionistic traits were not evident, making this descriptive evidence at the individual level.

The authors use the Interaction of Person-Affect-Cognition-Execution (I-PACE) model to describe the pattern as cognitive offloading, reduced confidence in independent judgment, and reinforcement of externalized thinking, accompanied by a subjective belief of declining skill and compromised functional autonomy. It brings a framework commonly used for addiction and behavioral dependence into the context of generative AI use, offering a descriptive explanatory path for this kind of reliance. The model application is an explanatory framework rather than a causal test; evidence comes from case-level behavioral description and patient self-report.

The report notes that repeatedly turning to family or friends for validation may lead to interpersonal fatigue, whereas AI tools have no such limits and are infinitely accessible, which might ultimately worsen the patient's ability to tolerate uncertainty. It identifies the availability features of AI (no fatigue, constant access) as a key difference from human sources of reassurance, suggesting they may amplify rather than relieve the anxiety cycle. A clinical inference based on case observation and theoretical reasoning; no control or quantitative data are provided in the text.

The authors suggest that given the ubiquity of AI tools and the prevalence of anxiety disorders, clinicians may need to be aware of the role AI tools could play in reassurance seeking and thus the perpetuation of anxiety. It brings generative AI use into the scope of clinical assessment and inquiry, as a practice-oriented reminder. A clinical recommendation derived from a single case, hypothesis-generating in nature and requiring further research.

This report applies to recognizing and conceptualizing patterns of generative AI reliance in anxious patients in clinical settings, particularly individuals with previously good functioning and without prominent personality disorder or perfectionistic traits; its value lies in offering a starting point for asking about AI use in clinical interviews and for understanding reassurance-seeking behavior through frameworks such as I-PACE.

Readers should keep in mind that this is a single case, so the causal direction between AI use and worsening anxiety cannot be determined; how the reliance pattern manifests across different populations, AI tools, and cultural contexts remains unclear; the applicability and predictive power of the I-PACE model in this context await testing; moreover, this reading is based on abstract-level text without figures or supplementary materials, and if those contain assessment instruments or timeline information, they could affect understanding of the course and severity.