AI Chatbots or Human Therapists? Belief-Based Predictors of Mental Health Help-Seeking Intentions in the Age of Generative AI

TL;DR

This study explores how beliefs about AI chatbots versus human therapists influence mental health help-seeking intentions, highlighting perceived emotional support and barriers like privacy and affordability across two diverse populations.

Contribution

It extends the Health Belief Model to compare dual modalities, revealing how perceptions and barriers differentially impact help-seeking decisions for AI and human therapists.

Findings

Perceptions of emotional support and personalization strongly influence help-seeking intentions.

Privacy and reliability concerns reduce intention to use GAI chatbots.

Affordability and structural barriers more strongly affect human therapy use.

Abstract

As generative artificial intelligence (GAI) enters the mental health landscape, questions arise about how individuals weigh AI tools against human therapists. This study examined belief-based predictors of intention to use GAI and therapists across two populations: a university sample (N = 1,155) and a nationally representative adult sample (N = 651). Using paired-sample t-tests following a MANOVA, we found that human therapists were viewed as providing greater emotional support and coping, relationship, and educational skills as well as being able to personalize treatment than GAI chatbots. In turn, GAI support was viewed as being more affordable and accessible. No differences between modalities were found with concerns about privacy, reliability, stigma, mental health literacy or help-seeking norms. Using LASSO regression, we examined how beliefs about each modality jointly shape help-seeking intentions. Across both samples, intentions to use either GAI or human therapists were most strongly associated with perceptions of interpersonal support, including emotional support, relational guidance, and personalization. Barriers differed across modalities: concerns about privacy and reliability were more strongly associated with reduced intention to use GAI, whereas structural constraints, particularly affordability, were more closely linked to human therapy use. These findings extend the Health Belief Model to a dual-modality context, demonstrating that help-seeking decisions reflect a comparative push-pull process in which barriers to one modality redirect users toward the other. Design implications are discussed for developing trustworthy, emotionally resonant GAI tools that complement rather than replace human care.