Date
Publisher
arXiv
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.
Study at a glance
- Question: What beliefs shape people's intentions to seek mental health support from generative AI compared to human therapists?
- Setting and data: 1,155 Southeastern U.S. university students and 651 nationally representative U.S. adults recruited online, surveyed in April–May 2025.
- Method: Survey analysis using MANOVA, paired t-tests, and regularized regression (LASSO) with stability selection.
Why it matters
Prior work often studies mental health apps or human therapists in isolation. This fragmented approach limits understanding of how people evaluate trade-offs between generative AI and human therapists when making help-seeking decisions.
Key findings
- Perceptions of emotional support and personalization were the strongest positive predictors of intentions to use either generative AI or human therapists across both samples.
- Concerns about reliability were the strongest negative predictor of intentions to use generative AI, whereas perceived affordability was the strongest positive predictor for therapist use.
- Dissatisfaction with one modality redirected users toward the other, as barriers to therapy predicted higher AI intentions and barriers to AI predicted higher therapist intentions.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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