This methodological cross-sectional study aimed to translate and conduct a preliminary psychometric evaluation of the Arabic version of the NURS-DIAL questionnaire among nursing students.

Convenience sampling was used to collect data from enrolled nursing students. Eligible students were invited to complete an online survey via institutional announcements and online learning platforms. The sample included undergraduate and postgraduate nursing students of all years of study and specializations in the field. The inclusion criteria were age 18 years or older, ability to read Arabic written language, and electronic informed consent. Exclusion criteria were students enrolled in nonnursing programs and students who participated in the pilot phase of NURS-DIAL. The final sample included 196 nursing students.

The final sample included 196 nursing students. Although this sample exceeded the absolute minimum of 100 participants, it was still small in relation to the complexity of the model. The final instrument contained 52 items across 5 dimensions. Therefore, the participant-to-item ratio was approximately 3.8:1. This ratio is less than the commonly recommended ratios of 5 to 10 participants per item and more conservative recommendations for complex CFA models. In other words, the sample size was adequate for the initial psychometric evaluation but not for the final confirmation of the factor structure. The CFA findings should be viewed as preliminary and verified in further studies using larger independent samples. Additionally, future studies should use a priori model-based sample size estimation or Monte Carlo power analysis that considers the number of factors and indicators, expected factor loadings, residual variances, correlations between factors, ordinal response categories, estimator type, and anticipated missing values.

The NURS-DIAL was created through a multinational research collaboration (AI4Health consortium) to evaluate nursing students’ knowledge of digital and AI literacy in 5 areas. Four previously used tools were integrated into the NURS-DIAL: the information literacy and digital well-being subscales of Martzoukou et al [2], Discipline-Based Digital Competence Survey Battery, mHealth Literacy Scale of Kim et al [16], Artificial Intelligence Literacy Scale of Wang et al [17], and Education Topics Needs Survey of Civaner et al [18] on AI.

The initiative involves nursing faculties and departments in several countries: UMIT TIROL in Austria, the University of Maribor Faculty of Health Sciences in Slovenia, and the University of Hail in Saudi Arabia, where the current sample was recruited. All participating institutions follow a common research protocol, obtain approval from their local ethics committees, and translate and culturally adapt the questionnaire for their own settings.

The consortium coordinating team manages the governance of the instrument, including curation of the item pool and cross-site coordination, while the project coordinators manage requests to use the instrument through the NURS-DIAL project website. Researchers and collaborating institutions can use the questionnaire at no cost. In the present study, the questionnaire was administered locally using Qualtrics and Google Forms. Parallel validation work is underway in additional cohorts, and preliminary findings have been reported in an Austrian cohort. To our knowledge, this study is the first to present an Arabic-language adaptation and preliminary psychometric evaluation of the NURS-DIAL.

The NURS-DIAL research team added 4 items to the AI Literacy subscale to better reflect AI literacy in the context of nursing and health care, as the original instrument was developed for general users, expanding the original 12-item scale [17] that measured AI literacy. Also, the AI Education Topics subscale was expanded from 15 to 20 items to include additional AI-related curricular priorities determined to be relevant to nursing education.

The Information Literacy subscale consists of 8 items, using a 5-point rating scale from novice to expert. The mHealth Literacy subscale consists of 6 items, using a 5-point rating scale from strongly disagree to strongly agree. The AI Literacy subscale consists of 16 items, using a 7-point rating scale from strongly disagree to strongly agree. The Digital Well-being subscale consists of 6 items, using a 5-point rating scale from novice to expert. The AI Education Topics subscale includes 20 items on a 5-point scale from include to no need describing the perceived priorities for AI content in the nursing curricula by students. Four items (AIL_c, AIL_g, DW_e, and AIE_t) were deleted after CFA, resulting in a final Arabic NURS-DIAL of 52 items.

The NURS-DIAL was translated and culturally adapted into Arabic using the 5-stage framework described by Beaton et al [15] for cross-cultural adaptation of self-report measures. Permission to translate, adapt, and publish the questionnaire was obtained from the AI4Health consortium, and copyright requirements were met.

In stage 1, 2 bilingual translators independently translated the original English NURS-DIAL into Arabic. Both translators were native Arabic speakers, fluent in English, and had backgrounds in nursing or health sciences. In stage 2, the interdisciplinary research team reviewed and reconciled the 2 translations. The review focused on terms related to digital technology, AI, and reverse-worded items to ensure clarity and cultural relevance in the Saudi Arabic version.

In stage 3, the reconciled Arabic version was independently back translated into English by 2 bilingual translators whose first language was English, who were fluent in Arabic, and who were unfamiliar with the original questionnaire. In stage 4, a multidisciplinary expert committee reviewed the forward translations, reconciled Arabic version, and back-translations. The committee assessed semantic, idiomatic, experiential, and conceptual equivalence and produced a prefinal Arabic version of the NURS-DIAL.

Six experts reviewed the prefinal 56-item Arabic NURS-DIAL for content validity. The panel included 3 nursing education academics with 8, 12, and 15 years of experience, a health informatics expert with 10 years of experience, a linguist with 7 years of experience, and a psychometrician with 11 years of experience. All held doctoral degrees and had experience in instrument development. Experts rated each item for relevance, clarity, and cultural appropriateness on a scale from 1 (not relevant) to 4 (highly relevant). The item-level content validity index (I-CVI) values for all items were at least 0.83, and the scale-level content validity index average was 0.94. Based on the expert review, minor wording changes were made to 4 items to improve clarity and cultural appropriateness for use in Saudi Arabia.

In stage 5, the prefinal Arabic NURS-DIAL was tested with 35 nursing students who met the same eligibility criteria as the main study sample. After completing the questionnaire, students commented on the clarity of the items, wording, response options, time needed to complete the questionnaire, and cultural relevance. It took approximately 15 to 20 minutes to complete. Students did not report any concerns about cultural appropriateness, and no items were added. The research team reviewed the feedback before the main psychometric evaluation. The Arabic items, English backtranslations, response options, scoring directions, and item-retention status are provided in Table S4 in Multimedia Appendix 1.

Participants completed a self-administered online survey using Qualtrics and Google Forms. The survey could be accessed at participants’ convenience using a smartphone, tablet, or computer. The survey link was sent out to nursing students via email, learning management system (LMS), and faculty posts for all program levels and years of study.

All invitation communications to standardize survey administration provided information on the study purpose, eligibility criteria, voluntary participation, and anticipated completion time and directed students to 1 online interface. The first page of the survey contained an information sheet and an electronic consent form, followed by items presented in a fixed sequence. The majority of closed-ended items were required; skip patterns were used only when conceptually justified; and the survey was designed for mobile accessibility to minimize missing data.

Statistical analysis of the psychometric properties of the Arabic version of the NURS-DIAL was carried out on the main validation dataset. After screening the data, special codes were treated as missing values, and some items were reverse scored. Missingness was patterned, increasing monotonically with item position consistent with survey dropout (Spearman ρ=0.995; Tables S1 and S2 in Multimedia Appendix 1); the primary CFA used weighted least squares mean and variance (WLSMV) with pairwise-present information, and the findings were robust to complete-case and multiple-imputation sensitivity analyses (Table S3 in Multimedia Appendix 1). Structural validity was evaluated with CFA using an appropriate estimator for ordinal data and standard fit indices. Internal consistency was evaluated by Cronbach α (≥0.70 was considered acceptable). Construct validity was assessed by intersubscale correlations for convergent and discriminant validity, while known-group validity was assessed by 2-tailed t tests and 1-way ANOVA on subscale scores.

In this study, sex was self-reported by participants in the online survey (male/female) and is used in the known-group validity analyses. Biological sex as recorded in official records was not assessed.

Prior to beginning data collection, ethical approval was obtained from University of Hail’s Institutional Review Board (case number H-2025‐784). As part of obtaining informed consent, each participant was required to complete an electronic form that included a brief description of the study’s purpose and procedures, potential risks/benefits of participating, as well as a statement indicating that participation was optional and that the student could withdraw or refuse to participate at any point during the completion of the survey and would incur no negative consequences. The survey was completed anonymously and did not include any identifying information.

This study provides preliminary evidence supporting the Arabic NURS-DIAL as a culturally adapted instrument for assessing digital and AI literacy among nursing students in Saudi Arabia. The findings support content validity; internal consistency reliability; and aspects of structural, construct, and known-groups validity. The implications of the findings should be interpreted considering the study’s methodological limitations.

The Arabic NURS-DIAL was developed through a structured translation and cultural adaptation process, including forward translation, reconciliation, back-translation, expert review, content validation, and student pretesting, in accordance with Beaton et al [15]. The content-validity findings and student feedback support the clarity and cultural acceptability of the Arabic version in this sample. The Arabic and English versions of the questionnaire, English backtranslations, response options, scoring directions, and item-retention status are provided in Table S4 in Multimedia Appendix 1.

The translation and adaptation process is important when assessing digital and AI literacy in Arabic-speaking nursing students. Previous studies in the Middle East and North Africa have explored attitudes toward AI, readiness to use AI, AI literacy, and willingness to use AI. Many of these studies used tools translated from English into Arabic but did not clearly explain how the Arabic versions were culturally adapted or psychometrically tested [9,19]. This study offers preliminary support for an Arabic tool that covers information literacy, mHealth literacy, AI literacy, digital well-being, and perceived AI education needs. Further studies are needed with larger and more diverse groups of Arabic-speaking nursing students.

The CFA showed initial support for the 5-factor model. Most fit indices were acceptable, but the SRMR was above the recommended cutoff. The 3 retained items AILeR, AILkR, and AIEpR had low loadings. This was related to the higher SRMR. These items need further review in a larger independent sample. No post hoc changes were made because the sample size was limited.

The subscales showed good reliability. Information literacy, mHealth literacy, AI literacy, and digital well-being had good internal consistency, while AI education topics had excellent internal consistency. This means that the items within each section were generally consistent in measuring the same area. These results support the use of the Arabic NURS-DIAL with nursing students for research and low-stakes educational assessment [20,21].

Construct validity was also supported by the relationships between the subscales. Information literacy, mHealth literacy, AI literacy, and digital well-being were positively related, suggesting that students who were more confident in 1 area often reported stronger skills in other digital areas. However, AI education topics showed weaker relationships with the other areas, especially information literacy. This suggests that students’ views about what AI topics should be included in nursing education are related to, but different from, their digital and AI skills. Overall, the findings suggest that the NURS-DIAL measures are related but separate areas of digital and AI literacy [22,23].

The known-groups analysis provided early, partial support for the validity of the Arabic NURS-DIAL. Female students scored higher than male students in mHealth literacy, which may suggest that the scale can identify sex-related differences in the use of digital health tools. This finding is consistent with Lupton and Maslen [24], who reported that women often use digital technologies to seek health information and support. However, because this difference was small, it should be interpreted cautiously. No clear sex differences were found in information literacy, AI literacy, digital well-being, or AI education topics. This is in line with Alshowkan and Shdaifat [13], who suggested that sex differences in digital and AI literacy may be decreasing as students have more similar exposure to technology in education.

Students’ views about AI education topics differed by year of study. This may indicate that students at different stages of the nursing program consider different AI topics important to their education. Bakarman et al [12] suggested that students may develop a greater understanding of and openness to AI as they gain clinical experience and progress through nursing education. However, the cross-sectional design did not allow the assessment of changes in students’ views over time.

The findings of El Arab et al [9] similarly showed that nursing students and nurses in the Middle East and North Africa generally had positive attitudes toward AI but reported gaps in knowledge, readiness, and experience using AI in clinical practice. In the present study, the Arabic NURS-DIAL identified lower AI literacy in areas related to privacy and the perceived difficulty of learning AI.

Apart from differences in AI education topics by year of study, no other year-group differences were observed. These exploratory findings require replication in larger and more diverse samples.

The findings suggest that the Arabic NURS-DIAL may help educators identify areas in which nursing students require further support in digital and AI literacy.

First, the 5 subscales may provide information for educators and curriculum committees about students’ strengths and learning needs. In this study, students had lower scores in information literacy, suggesting a need for learning activities focused on searching for information, evaluating information sources, and referencing academic work. Students also showed lower AI literacy in areas related to AI privacy and the perceived difficulty of learning AI. Educational activities addressing the safe use of AI, privacy, and the critical evaluation of AI-generated information may therefore be beneficial.

Second, students in different years of the nursing program had different views about which AI topics were important. AI content can be introduced gradually as students move through the program. Topics such as clinical decision support, ethics, and checking AI-generated information are more suitable for students with more clinical experience. Assessing students at different stages can also help faculty see whether curriculum changes improve digital and AI literacy over time.

There are currently no normative data or validated cutoff scores for the NURS-DIAL. Therefore, scores should be used to identify learning needs and support teaching, rather than for diagnosis, institutional comparison, or high-stakes decisions.

This study has several strengths. The Arabic NURS-DIAL was translated and culturally adapted using an established framework, reviewed by a multidisciplinary expert panel, and pretested with nursing students. The study also evaluated several psychometric properties, including content validity, structural validity, internal consistency reliability, construct validity, and known-groups validity. In addition, CFA was conducted using an estimator appropriate for ordinal data.

The findings should be interpreted in light of several limitations. The sample was recruited by convenience sampling from 1 university and consisted of undergraduate students in general nursing, which limits generalizability to other institutions, educational levels, specialties, and Arabic-speaking populations. The sample size was also limited in relation to the 52-item, 5-factor CFA model. Therefore, the factor structure should be considered preliminary. Test-retest reliability, criterion-related validity against objective digital or AI competence measures, and measurement invariance across groups were not assessed. Future studies should use multi-institutional and more diverse samples to confirm the factor structure, examine measurement performance across subgroups, and evaluate stability over time.

The findings provide preliminary support for the Arabic NURS-DIAL as a measure of digital and AI literacy among nursing students in the Saudi setting studied. The evidence for content validity, structural validity, internal consistency reliability, and construct validity was obtained. The limited sample size and single-institution design require cautious interpretation. At present, the instrument is most appropriate for exploratory research and formative assessment in comparable settings. Multi-institutional studies are needed to confirm the factor structure and establish its suitability for broader use.