Digital and AI Literacy in Nursing Education: How Students Assess Themselves

Nurses are expected to help shape digital tools and AI in their everyday work. How well prepared do nursing students feel for this? For the first time, BFH has used the questionnaire of the international NURS-DIAL study in German : most of the 71 participating students rate themselves as competent in using digital tools and AI, but are uncertain about ethics and data protection.

Digital literacy and AI literacy

Digital transformation is having a profound impact on care processes in the nursing profession. A wide range of application scenarios are digitalising everyday processes, for example technical assistance systems, robotic systems, telemedical forms of communication (telecare) and electronic nursing documentation (Bitterlich & Götze, 2022). These digital tools are implemented sustainably and effectively particularly where they create a tangible benefit for nurses and patients. Generating this benefit requires nurses to have the relevant (digital) competencies that enable them to take an active part in the development, testing, implementation and evaluation of digital tools in everyday nursing practice (Thilo et al., 2024).

Consequently, there is a need to adapt the educational content of nursing degree programmes and to define updated competency profiles. In order to generate evidence-based recommendations for the development and strategy of teaching, the international NURS-DIAL survey (NURSing Digital and AI Literacy Assessment) was conducted, coordinated by the Faculty of Health Sciences at the University of Maribor (Slovenia). The study set out to assessthe level of digital literacy and artificial intelligence (AI) literacy among international nursing students in bachelor’s and master’s programmes. Bern University of Applied Sciences (BFH) took part in this international cross-sectional study by translating the questionnaires into German for the first time in collaboration with UMIT TIROL- the private University of Health Sciences and Technology Tyrol, and, by conducting an initial Swiss survey among Bachelor of Science (BSc) and Master of Science (MSc) students on the BFH’s Nursing programme for validation purposes.

Method

In October and November 2025, all nursing students enrolled at BFH (BSc and MSc) were invited by email via the learning platform to take part in an anonymous online survey. The questionnaire comprised 66 items on participants’ self-assessment. In addition to demographic data, it covered six topic areas:

• Information literacy: searching for information in a targeted way, evaluating it critically and using it responsibly (5-point scale from ‘beginner’ to ‘expert’)
• mHealth literacy: understanding and evaluating health information from apps and mobile devices and using it for decision-making (5-point agreement scale)
• AI literacy: using AI applications competently, recognising AI in products and taking ethical and data protection aspects into account (7-point agreement scale)
• Digital wellbeing: being able to use digital technologies in a way that does not impair relationships, health and safety (5-point scale from ‘beginner’ to ‘expert’)
• Integration of AI topics into education: which content should be part of the degree programme, including participants’ own suggestions (5-point scale)
• Personal perceptions of and experiences with AI: open-ended questions

Only fully completed questionnaires were analysed descriptively using R and separately by the level of study. For each topic area, an overall score was calculated from the individual items.

Results

A total of 71 students took part in the survey, 30 (42.3%) of whom were enrolled in the BSc programme and 41 (57.7%) in the MSc programme (response rate 11.5%). The BSc students had a mean age of 25.1 years (range 18–44 years), the MSc students 33.6 years (range 24–50 years); 90% of participants are female. MSc students rate themselves equally high or slightly higher than BSc students in all four topic areas. More than a third of the students surveyed were in their first year of study (38%), and two thirds had specialised in general/acute care (66.2%).

Table 1 presents and compares the overall scores of BSc and MSc students for the topic areas information literacy, mHealth literacy, AI literacy and digital wellbeing. It shows that MSc students rate their knowledge and competencies at least as high as, or higher than, BSc students.

Table 1: Overall scores of BSc and MSc students in four topic areas

Information literacy: The overall score for the questions on information literacy was 2.8 for BSc students and 3.2 for MSc students, placing it between basic knowledge (2) and intermediate knowledge (3). In general, MSc students rated their information literacy higher than BSc students.

The largest differences emerged in citing digital sources and in correctly sharing information publicly: around 45% and 50% of BSc students respectively rated themselves as ‘beginner’ or as having ‘basic knowledge’, compared with 20% and 35% of MSc students.

mHealth literacy: The overall score for the questions on mHealth literacy was 3.5 for BSc students and 3.8 for MSc students. Students at both levels frequently use health apps. Around 75% are familiar with health information and apps on mobile devices, and around 65% feel confident assessing their quality. They are more hesitant when it comes to using data collected via mobile devices for health-related decisions: around 22% of BSc students and 13% of MSc students reject this.

AI literacy: The overall score for the questions on AI literacy was 4.7 for BSc students and 4.9 for MSc students. Overall, the majority of students at both levels rated themselves as highly competent in using AI and in knowing where and how to use it meaningfully. Approximately 75% of MSc students and approximately 60% of BSc students rated their ability to use AI applications and products competently to support their daily work at 5–7. BSc students are less confident when it comes to ethical questions and recognising AI in apps and products: around 13% of them disagree (scores 1–3) with the statement that they always adhere to ethical principles when using AI, compared with 5% of MSc students.

Integration of AI topics into education: Both groups consider AI topics in their studies to be important, although MSc students do so considerably more strongly: around 70% of them believe that AI knowledge and skills should definitely be integrated, compared with around 40% of BSc students. Around 70% of both groups consider the critical evaluation of AI output to be important. BSc students are more sceptical about specialised clinical applications such as robot-assisted procedures, social robots in home care, AI-supported psychiatric diagnostics or genetic risk assessment: depending on the topic, 10–25% of them reject integration, compared with a maximum of 10% of MSc students.

Digital wellbeing: Both groups achieve an overall score of 3.3, just above intermediate knowledge. Most students see themselves as advanced in maintaining healthy relationships online and offline and in recognising that digital information can cause stress.

In the supplementary free-text responses, students from both programmes described very heterogeneous feelings in relation to AI, ranging from positive (e.g. a sense of security, support, encouragement) to negative (e.g. a sense of uncertainty, being overwhelmed, scepticism or ambivalence).

Conclusion: What the results mean for teaching

Assessing nursing students’ digital and AI‑related competencies allows teaching content to be tailored to the identified knowledge gaps and corresponding practice requirements.. In the present study, an instrument developed specifically for nurses was designed internationally and validated simultaneously in several languages. The data from this validation dataset provide initial indications and reveal trends. This article highlights the thematic priorities on which international research (Dong, 2026; El-Banna et al., 2025; Zhang et al., 2026) is currently focusing in the field of digital and AI literacy in the nursing profession. Furthermore, the study provides a scientifically validated German-language instrument and thus makes an important contribution to measuring digital and AI-related competencies in the nursing context.

Interestingly, it should be noted that the MSc students’ higher self-assessment in information literacy  is likely to be related to repeated practice during their studies. It could also be concluded that it would be worthwhile to examine opportunities for intensive practice in the BSc programme. The dataset showed a high use of mobile health apps among students, a basic awareness of digital risks , and uncertainties regarding data protection and ethical issues.

At the same time, an ambivalent attitude towards digital wellbeing persists, as has already been described in earlier scientific literature (Pei et al., 2025; Vanden Abeele, 2021). Specifically, according to the dataset analysed, digital transformation is consistently associated with opportunities, i.e. efficiency or support, as well as with risks, i.e. dehumanisation, ethical dilemmas or susceptibility to error. In addition, students’ uncertainty increases with more complex applications such as clinical decision-making, genetic risk assessment or robot-assisted procedures.

There may be a need here for more practice-oriented, concrete, successful and beneficial applications in routine care, which can then in turn become part of education.

References

Bitterlich, S., & Götze, U. (2022). Pflege 4.0 – Digitale Transformation eines Pflegeunternehmens. In T. Kümpel, K. Schlenkrich, & T. Heupel (Eds.), Controlling & Innovation 2022 (pp. 27–60). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-36484-7_2

Dong, X. (2026). Artificial intelligence literacy of nursing students: A cross-sectional study. Nurse Education Today, 166, 107272. https://doi.org/10.1016/j.nedt.2026.107272

El-Banna, M. M., Sajid, M. R., Rizvi, M. R., Sami, W., & McNelis, A. M. (2025). AI literacy and competency in nursing education: Preparing students and faculty members for an AI-enabled future-a systematic review and meta-analysis. Frontiers in Medicine, 12, 1681784. https://doi.org/10.3389/fmed.2025.1681784

Pei, X., Guo, J., & Wu, T.-J. (2025). How Ambivalence Toward Digital–AI Transformation Affects Taking-Charge Behavior: A Threat–Rigidity Theoretical Perspective. Behavioral Sciences, 15(3), 261. https://doi.org/10.3390/bs15030261

Thilo, F. J. S., Ranegger, R., & Hackl, W. (2024). Künstliche Intelligenz für die Pflege. Krankenpflege | Soins Infirmiers | Cure Infermieristiche. https://doi.org/10.24451/arbor.21349

Vanden Abeele, M. M. P. (2021). Digital Wellbeing as a Dynamic Construct. Communication Theory, 31(4), 932–955. https://doi.org/10.1093/ct/qtaa024

Zhang, M., Woodcock, D., & O’Connor, S. (2026). Using Generative Artificial Intelligence (GenAI) to Co-Design Digital Health Technologies: Some Lessons Learned. Computers, Informatics, Nursing: CIN. https://doi.org/10.1097/CIN.0000000000001537