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Development and psychometric validation of the barriers to AI integration in nursing education scale (BAI-NE): a multi-institutional study

Year: 2026

Type: article

Abstract: This multi-institutional study aimed to develop and psychometrically validate the Barriers to AI Integration in Nursing Education Scale (BAI-NE). A methodological study with a mixed-methods exploratory design was conducted in two phases: scale development and content validation, followed by psychometric evaluation. The 15-item BAI-NE was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and an extensive literature review. Content validity was assessed by a panel of ten experts. A convenience sample of 470 nurse educators from seven faculties of nursing participated in the study. The sample was randomly divided for Exploratory Factor Analysis (EFA; n = 235) and Confirmatory Factor Analysis (CFA; n = 235). EFA identified a four-factor structure explaining 42.7% of the total variance, comprising Technical and Infrastructure Barriers, Knowledge and Training Barriers, Ethical and Regulatory Barriers, and Attitudinal and Organizational Barriers. EFA factor loadings ranged from 0.566 to 0.738. CFA provided preliminary support for the proposed model, with good fit indices (χ²/df = 0.90, RMSEA = 0.00, CFI = 1.000, TLI = 1.015, SRMR = 0.043, GFI = 0.961); because χ² was lower than df and CFI and RMSEA were at their bounds, these values should be interpreted cautiously and replicated in independent samples. Standardized CFA loadings ranged from 0.54 to 0.82. Internal consistency was satisfactory, with Cronbach’s alpha values ranging from 0.713 to 0.754 and McDonald’s omega values ranging from 0.715 to 0.754. Test–retest reliability over a two-week interval was excellent for all subscales (ICC = 0.85–0.92). Convergent validity was modest, with composite reliability values exceeding 0.70, while AVE values ranged from 0.426 to 0.451. Discriminant validity was supported using the Fornell-Larcker criterion and HTMT values < 0.85. Among the four subscales, Knowledge and Training Barriers received the highest mean score (M = 14.0, SD = 2.51), followed by Attitudinal and Organizational Barriers (M = 13.1, SD = 2.63), Technical and Infrastructure Barriers (M = 12.8, SD = 2.68), and Ethical and Regulatory Barriers (M = 9.1, SD = 2.10). The BAI-NE demonstrated preliminary evidence of satisfactory validity and reliability and provides a practical instrument for identifying barriers to AI integration in nursing education, informing faculty development initiatives, institutional planning, and future research. The BAI-NE is intended to be scored and interpreted at the subscale level only.

Source: Scientific Reports

DOI: 10.1038/s41598-026-76573-y

Authors: Mona Gamal Mohamed, Zahra Abdirahman Mohamud, Marwa Samir Sorour

Institutions: Ras al-Khaimah Medical and Health Sciences University, Salaam University, Somalia National Bureau of Statistics, Tanta University

Language: English

Topic: Health Education and Validation

Subfield: Education

Field: Social Sciences

Domain: Social Sciences

SDGs: Good health and well-being, Quality education

Open Access status: gold

Estimated APC paid: $2,690