Development of a scale for measuring the perception of artificial intelligence among mental health consumers - Reema Al-Daraiseh; Wafa’a Ta’an; Tareq Mukattash; Mohammed M. Al-Hammouri; Rana Abu-Farha; Brett Williams (PLoS ONE, 2026)
Background Artificial Intelligence (AI) has emerged as a transformative force revolutionizing various sectors, including healthcare, particularly the mental health field. However, the acceptance and integration of AI technologies in different healthcare systems can be influenced by various factors, including cultural, social, and individual aspects. Nevertheless, there is a need for a valid and reliable tool for assessing AI’s perception among healthcare consumers. Aim To develop and validate a tool for the perception of AI among healthcare consumers and apply the tool to assess AI’s perception among mental health consumers in the Jordanian healthcare system. Method A cross‐sectional descriptive correlational design was utilized in the study. Data was collected from a convenience sample of 431 mental health consumers visiting mental health clinics of the International Medical Corps and university hospitals in Jordan. Structured interviews were conducted using an AI Perception (AIP) questionnaire developed by the authors. The questionnaire’s content validity was assessed by an expert panel. Using Principal Component Analysis (PCA), the construct validity of the tool was evaluated, and its internal consistency was examined using Cronbach’s alpha. Descriptive statistics were used to assess the levels of AI perception among participants. Results The final AIP tool consisted of 20 items across 4 domains and has demonstrated strong internal consistency across its four domains: AI acceptance and readiness (α = 0.92), AI perceived importance (α = 0.92), AI perceived risk (α = 0.9), and AI perceived challenges (α = 0.85). The construct validity of the four-domain structure of the tool was supported by PCA. Additionally, the mean scores for each domain indicated the average level of agreement with AI perception items among participants. Specifically, the mean score for AI acceptance and readiness was ( 2 . 7 ± 0 . 96 ). AI perceived importance was (2.18 ± 0.83), AI perceived risk was (2.58 ± 0.92), and AI perceived challenge was (2.78 ± 0.87). Conclusion The findings of this study resulted in developing a valid and reliable 20-item tool to assess AI’s perception among mental health consumers. The tool can be used to assess the predictors of AI’s readiness among mental health consumers. Therefore, aiding policymakers and other stakeholders in understanding the AI adoption barriers from the perspective of end-users. In addition, this study developed the AIP tool that can be validated and used among other populations in future research.