JMIR Publications

ABSTRACT

Background:

Background:

Generative Artificial Intelligence (GenAI) can automate time-intensive tasks and support clinical decision-making in care settings. To respond effectively to these developments, nurses require appropriate competencies to ensure that integration of GenAI strengthens care quality and patient safety. However, validated literacy assessment tools are lacking. Therefore, strengthening nurses’ understanding and application of GenAI is essential to promote its safe use in the nursing profession.

Objective:

Objective:

To develop and psychometrically validate the Generative Artificial Intelligence Literacy Scale for Nurses.

Methods:

Methods:

We conducted a two-phase, cross-sectional online survey of registered nurses nationwide in Taiwan. Phase one involved conceptualization and item generation based on a literature review, followed by content appraisal through expert discussion with six external reviewers. A 50-item pool was generated. Subsequently, five external reviewers evaluated content validity. Items with a content validity index<0.78 or flagged for revision were revised or deleted. Phase two evaluated psychometric properties (item analysis, internal consistency, split-half reliability, and criterion-related validity) and construct validity via exploratory factor analysis (loading ≥0.60), followed by confirmatory factor analysis (CFA).

Results:

Results:

In phase one, the initial 50 items underwent expert content validation and were revised to 46 items (Scale content validity index/Average=0.92). In phase two, 1,313 questionnaires were collected, of which 191 invalid responses were excluded; thus, 1,122 valid responses were analyzed. Extreme group comparison (top and bottom 27%) revealed statistically significant differences for each item (p<.001). The final scale comprised 25 items across six dimensions: (1) Responsible Use; (2) Updated Competencies; (3) Critical Evaluation; (4) Risk Identification; (5) Fundamental Knowledge; (6) Ethics and Law. The cumulative variance explained was 64.1%. Initial CFA indicated good model fit: RMSEA=0.038 with the 90% confidence interval 0.033-0.044, SRMR=0.04, CFI=0.99, GFI=0.93, AGFI=0.92, NNFI=0.99. The scale was moderately correlated with the Short Form Meta Artificial Intelligence Literacy Scale (r=0.61, p<.001). Reliability was excellent (total scale: Cronbach α=0.93; McDonald ω=0.92; split half, Spearman–Brown=.94).

Conclusions:

Conclusion: The scale is a concise, nurse-specific instrument with strong psychometric properties across six clinically relevant domains. It supports needs assessment, targeted training design, intervention evaluation, and longitudinal monitoring to promote the safe and ethical use of GenAI in nursing.