Building digital competence for future healthcare through hands-on AI experience

Abstract

Introduction Digital competence is critical in health professions education. This study examined how structured, hands-on engagement with AI influences master’s students’ perspectives on its use in practice and study, employing the DigCompEdu framework and considering innovation competence as a learner-oriented skill. Materials and methods Pre-and post-surveys were conducted in an interprofessional health–technology course (baseline n = 47; end-survey free-text n = 34). The baseline items assessed prior exposure and expectations, while the end-survey narratives were analysed using reflexive thematic analysis (inductive, semantic). DigCompEdu served as a guide for interpretation rather than as a strict codebook. Innovation competence was defined as the ability to identify opportunities, experiment with small changes while ensuring they work, and collaborate across disciplines. Results Five key themes emerged, reflecting students’ future orientation: (1) Future clinical integration—AI integrated into routine work (such as decision support, documentation, and monitoring), while maintaining human judgement; (2) Learning trajectories—Ongoing use of AI for tasks like scoping, structuring, and summarising, along with verification routines and boundaries to prevent over-reliance; (3) Professional identity & interprofessional roles—AI viewed as a collaborator with a readiness to bridge clinical and engineering perspectives; (4) Ethical guidelines and supports—Calls for guidelines, increased governance literacy, and the provision of time and tools for verification; and (5) Innovation in practice—Small-scale AI-supported innovations tested through iterative trials. These themes primarily aligned with A6 (Facilitating learners’ digital competence), particularly A6.1 (information and media literacy), A6.3 (digital content creation), A6.4 (responsible use), A6.5 (digital problem solving), along with contributions from A1 (Professional engagement), with contributions from A2/A3; A4 (Assessment) was less represented. The baseline context highlighted limited prior exposure to AI, with 64% of the participants reporting little or no AI experience. Conclusion Short, hands-on AI experiences, when combined with clear ethical guidelines, can foster the development of skills needed for the future and contribute to an AI-enhanced professional identity. Programmes should align their learning outcomes with the DigCompEdu framework. It is essential to pair hands-on AI with verification processes and brief primers on governance and ethics. Additionally, programmes should practice service and process innovation, such as Plan-Do-Study-Act cycles, implementation canvases, and usability walk-throughs, through interprofessional co-design. Addressing the A4 gap (Assessment) with low-stakes activities that provide AI-feedback compared to traditional rubrics is also necessary. These strategies will help graduates become not only proficient in using the tool, but also ethically grounded, capable of innovation, and prepared to career in AI-enabled healthcare.

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