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Replacing written assessment with an oral ISBAR assessment in a 700‑student cohort: cohort‑level outcomes for performance, integrity, and staff experience
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DOI：10.1080/02602938.2026.2684284.png)
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Abstract
En 中文
Generative artificial intelligence (GenAI) has intensified concerns about the authenticity and integrity of written assessment in higher education. These challenges are particularly relevant in nursing, where safe practice requires clinical reasoning and real‑time decision‑making. This practice‑based evaluation examined the redesign of a written case study into an authentic, oral ISBAR (Identify, Situation, Background, Assessment, Recommendation) assessment in a second‑year undergraduate nursing unit (n = 740). Drawing on routine institutional data including cohort‑level performance outcomes, implementation records, and student and educator feedback, the evaluation assessed whether the oral assessment maintained academic standards while strengthening academic integrity and improving staff experience. Findings demonstrated comparable cohort‑level performance across written (2024) and oral (2025) formats, with stable overall unit means and improved end‑of‑semester examination results. No breaches of academic integrity were identified under the oral format, which incorporated verified identity conditions, invigilated perusal, multiple case variants, and real‑time performance. Educators reported greater confidence in authorship, stronger alignment between assessment evidence and clinical judgement, and a more engaging marking experience; the redesign reduced marking time and cost per student. These results indicate that oral, performance‑based assessment offers a feasible, scalable, and pedagogically aligned response to integrity challenges in GenAI‑mediated learning environments while preserving academic standards.
Keywords:
Authentic assessment
academic integrity
generative AI
oral assessment
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