Building Digital Competence for Future Healthcare through hands-on AI experience

Yngve Røe^1*Marko Lukic²Safora Johanssen³Wilfried Admiraal⁴Minna Pikkarainen⁵

• ¹Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
• ²Department of Community Medicine, UiT Norges arktiske universitet, Tromsø, Norway
• ³Department of Life Sciences and Health–Radiography, Oslo Metropolitan University, Oslo, Norway
• ⁴Centre for the Study of Professions –Teaching and Research, Oslo Metropolitan University, Oslo, Norway
• ⁵Department of Rehabilitation Science and Health Technology, Department of Product Design, Oslo Metropolitan University, Oslo, Norway

Introduction: Digital competence is critical in health-professions education. This study examined how structured, hands-on engagement with AI shapes master’s students’ views of its use in practice and study, using DigCompEdu as a sensitising lens and treating innovation competence as a learner-oriented, service/process skill. Materials and Methods: Pre–post surveys were conducted in an interprofessional health–technology course (baseline n = 47; end-survey free-text n = 34). Baseline items assessed prior exposure and expectations. End-survey narratives were analysed with reflexive thematic analysis (inductive, semantic). DigCompEdu informed interpretation (not a deductive codebook). Innovation competence was defined as the ability to identify opportunities, trial small changes with verification, and collaborate across disciplines. Results: Five themes captured students’ future orientation: (1) future clinical integration - AI embedded in routine work (decision support, documentation, monitoring) with human judgement retained; (2) learning trajectories - ongoing use of AI for scoping/structuring/summarising, paired with verification routines and boundaries to avoid over-reliance; (3) professional identity & interprofessional roles - AI as “collaborator” and readiness to bridge clinical and engineering perspectives; (4) ethical guardrails & supports—calls for guidelines, governance literacy, and time/tools for verification; and (5) innovation-in-practice - small, AI-supported workflow changes tested iteratively. Themes mapped chiefly to A6 (Facilitating learners’ digital competence) - notably A6.1 information & media literacy, A6.3 digital content creation, A6.4 responsible use, A6.5 digital problem solving - and A1 (Professional engagement), with contributions from A2/A3; A4 (Assessment) was under-represented. Baseline data showed limited prior exposure to AI, with 64% reporting little or none. Conclusions: Short, authentic AI experiences can catalyse future-ready competence and an AI-augmented professional identity when coupled with explicit ethical guardrails. Programmes should align learning outcomes with DigCompEdu; pair hands-on AI with verification routines and brief governance/ethics primers; rehearse service/process innovation (e.g., Plan-Do-Study-Act cycles, implementation canvases, usability walk-throughs) through interprofessional co-design; and address the A4 gap with low-stakes, AI-feedback-vs-rubric activities to strengthen evaluative judgement. These strategies help graduates become not only proficient in using AI, but also ethically grounded, innovation-capable, and prepared for careers in AI-enabled healthcare.