2026-10-13T10:41:02Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33718726 2026-09-14T05:29:42Z category_173 portal_316 item_type_3 month_year_09_2026

Supplementary file 1_Heterogeneity in AI attitudes, anxiety, and acceptance among psychology students and psychotherapy trainees: a domain-specific latent class analysis.docx Merve Armagan-Bogatekin (24868075) Applied Psychology artificial intelligence job-related anxiety psychology students psychotherapy psychotherapy trainees technology acceptance Introduction<p>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.</p>Methods<p>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.</p>Results<p>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.</p>Discussion<p>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.</p> 2026-09-14T05:29:42Z Dataset Dataset 10.3389/fpsyg.2026.1859806.s001 https://figshare.com/articles/dataset/Supplementary_file_1_Heterogeneity_in_AI_attitudes_anxiety_and_acceptance_among_psychology_students_and_psychotherapy_trainees_a_domain-specific_latent_class_analysis_docx/33718726 CC BY 4.0