Beyond AI Detection: A Decision-Making Guide for Strategic Assessment Design in Nursing Education - Katherine Ann McCusker; Betsy B. Kennedy; Abby Parish (Nurse Educator, 2026)
Background: The use of artificial intelligence (AI) by pre- and postlicensure nursing students threatens the validity of assessments and raises concerns about competency-based evaluation and public safety. Problem: Nurse educators lack a structured framework for evaluating the degree to which specific assessments are vulnerable to inappropriate AI use and for prioritizing where assessment design efforts are most needed. Approach: Graduate nursing faculty used a backward design process, anchoring criterion development in known low-vulnerability assessments and refining criteria through iterative review and applied testing across multiple assessment types. Outcomes: The Assessment and AI Vulnerability Decision-Making Guide for Nursing produces a total vulnerability score across 2 domains—assessment setting and assessment method—paired with a targeted improvement guide for faculty. Conclusions: This decision-making guide enables nursing educators to strategically direct assessment design efforts toward high-stakes contexts where AI poses the greatest risk to the validity of competency evaluation, while preserving pedagogical flexibility when AI use aligns with professional practice.