Abstract:

Background:
With the rapid integration of digital and AI technologies in healthcare, nursing students must develop digital and AI literacy competencies early in their educational training. These skills are crucial for adapting to evolving healthcare practices, ensuring patient safety, and fostering efficient care delivery. Current research points to an urgent need for standardizing digital literacy and AI competency training. This study is the South African leg of a collaboration with the University of Maribor, Slovenia.

Aim:
Assess current levels of digital literacy and AI competencies among nursing students at a selected university in the Western Cape, by investigating their levels of digital information literacy skills, mhealth literacy skills, AI literacy skills, digital wellbeing; as well as the correlation between AI and digital health literacy and the topics to be integrated into AI education in nursing.

Method:
A cross-sectional survey design will be used. Using all-inclusive sampling, under- and postgraduate nursing students (n = 267-377; Margin of error = 5-6%; Confidence level 95% and response distribution 50%) from the selected university will complete a survey that contains several instruments that collect data on general demographics; a number of validated scales, namely, Information Literacy Scale (Martzoukou et al., 2024), digital and mhealth literacy scale  extracted from miHERS (Cronbach α values ranging from 0.84 to 0.91)(Kim et al., 2024), Artificial intelligence literacy scale (Wang et al., 2023), Digital wellbeing (Martzoukou et al., 2024)and AI education in nursing (Civaner et al., 2022). The scales have established validity and reliability and have been tested in various settings (Civaner et al., 2022; Kim et al., 2024; Martzoukou et al., 2024; Wang et al., 2023), and the MAIRS (Karaca et al., 2021). Mean ratings and confidence intervals will be calculated for each of the different scales (Information literacy, mobile literacy, AI literacy, Digital Wellbeing) and for each item in the different scales scale. Associations between demographics and total Scores for each scale will be tested using relevant non-parametric statistics. Correlations between AI, digital and mHealth literacy will be determined using Pearsons correlation. The statistical analysis will be performed by using IBM SPSS Statistics v28, and R-4.0.3. The confidence interval (CI) will be set at 95%, and p<.05 will be considered statistically significant. 

Preliminary findings:
To date 170 responses (male = 27, 15.8%, female = 144, 84.2%) were received. Most respondents were undergraduate students (129, 75.9%). Respondents with post-graduate diplomas (129, 75.9%), master degrees (17, 10.0%) or PhDs (13, 7.6%) made up the rest of the responses. Most respondents rated their level of experience (novice, basic knowledge, intermediate, advanced, expert), they mostly rated themselves as ‘intermediate’ followed by ‘advanced’ – ‘novice’ was least often rated, followed by ‘expert’. Where respondents rated their agreement (strongly disagree – strongly agree) with statements, such that to disagree would suggest lesser competence/literacy and to agree would suggest more competence/literacy, they mostly responded ‘agree’, followed by ‘neutral’. Overall, the current data suggests that nursing students may have at least an intermediate level of competence/literacy.

Keywords:

Digital, technology, AI, nursing education.