Constructing digital professionalism in the generative AI era: A qualitative study with nursing students and faculty

Generative artificial intelligence (AI) is entering coursework, simulation, and assessment in nursing programs. Conventional digital professionalism focused on online conduct now requires visible accountability for algorithmic assistance to protect learning, integrity, and equity.

To explore how pre-licensure nursing students and faculty conceptualize and enact digital professionalism when using generative AI in coursework, simulation, and assessment, and to develop an explanatory model to inform policy, pedagogy, and assessment.

Qualitative phenomenological study.

Twenty-four participants from a public college of nursing and affiliated teaching sites in Saudi Arabia (16 students, 8 faculty) completed in-depth, semi-structured interviews in Arabic or English. Audio-recorded interviews were transcribed, translated as needed, and analyzed thematically using Braun and Clarke's six-phase approach supported by NVivo. Trustworthiness was enhanced through maximum-variation sampling, member reflections, an audit trail, and analyst triangulation. Reporting followed SRQR.

Four themes described how participants stabilized professional practice with AI. 1) Professionalism as transparent practice: disclosure as academic provenance and verification as routine ethical habit. 2) Uneven rules, uneven risks: inconsistent course policies and tacit norms prompting avoidance or minimal disclosure, with greater perceived risk for language learners and less resourced students. 3) Human-in-the-loop professionalism: right-sized guardrails that fit real workloads, including brief provenance notes, proportionate verification, no-upload rules for protected materials, and human-led debriefing and marking. 4) Becoming a professional with AI: identity work from stigma to legitimacy supported by shared vocabulary, model disclosures, and concise verification checklists. These findings informed a simplified model in which disclose, verify, and revise under human authorship are enabled when guardrails are feasible and expectations explicit.

Digital professionalism with AI functions as a teachable accountability process. Clear, proportionate expectations promote integrity, assessor independence, and improved equity.