Frontiers in psychology · 2026
Heterogeneity in AI attitudes, anxiety, and acceptance among psychology students and psychotherapy trainees: a domain-specific latent class analysis.
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Armagan-Bogatekin Merve
Аннотация
INTRODUCTION: Artificial intelligence (AI) is increasingly proposed as a support tool in psychotherapy, yet little is known about how psychology students and psychotherapy trainees psychologically orient toward such tools beyond aggregate acceptance scores.
METHODS: This study used latent class analysis (LCA) to explore heterogeneity in psychology students' and psychotherapy trainees' (N = 286) attitudes toward AI, technology acceptance, readiness, and anxiety in relation to two AI-supported clinical tools: an automated feedback tool and a treatment-recommendation tool. Six separate latent class models were estimated across the domains of general AI attitudes, technology acceptance, readiness, job-related anxiety, learning anxiety, and concerns about AI risk and autonomy.
RESULTS: Across domains, three- to four-class solutions consistently emerged, indicating that participants cannot be described by a single, uniform orientation toward AI. Across several domains, moderate or mixed response patterns were prominent, whereas other classes reflected more favorable or unfavorable orientations, including skepticism, elevated learning- or job-related anxiety, or heightened concern about AI risk. Because each domain was modeled separately, these results describe domain-specific latent response patterns rather than a single integrated psychological profile spanning acceptance and anxiety simultaneously.
DISCUSSION: Findings are discussed in relation to the Unified Theory of Acceptance and Use of Technology (UTAUT), technostress, and professional-identity-threat perspectives, and suggest that training for AI-supported psychotherapy tools may need to be tailored to distinct subgroups rather than a uniform trainee population. Given the exploratory, cross-sectional, secondary-data design, findings should be treated as hypothesis-generating rather than confirmatory.
METHODS: This study used latent class analysis (LCA) to explore heterogeneity in psychology students' and psychotherapy trainees' (N = 286) attitudes toward AI, technology acceptance, readiness, and anxiety in relation to two AI-supported clinical tools: an automated feedback tool and a treatment-recommendation tool. Six separate latent class models were estimated across the domains of general AI attitudes, technology acceptance, readiness, job-related anxiety, learning anxiety, and concerns about AI risk and autonomy.
RESULTS: Across domains, three- to four-class solutions consistently emerged, indicating that participants cannot be described by a single, uniform orientation toward AI. Across several domains, moderate or mixed response patterns were prominent, whereas other classes reflected more favorable or unfavorable orientations, including skepticism, elevated learning- or job-related anxiety, or heightened concern about AI risk. Because each domain was modeled separately, these results describe domain-specific latent response patterns rather than a single integrated psychological profile spanning acceptance and anxiety simultaneously.
DISCUSSION: Findings are discussed in relation to the Unified Theory of Acceptance and Use of Technology (UTAUT), technostress, and professional-identity-threat perspectives, and suggest that training for AI-supported psychotherapy tools may need to be tailored to distinct subgroups rather than a uniform trainee population. Given the exploratory, cross-sectional, secondary-data design, findings should be treated as hypothesis-generating rather than confirmatory.