Background Writing high-quality test items is essential for valid assessments in nursing education but remains time-intensive and challenging for faculty with limited training. Aim To evaluate the effectiveness of an AI-assisted item writing tool, ATI's Claire AI, by addressing reductions in writing time, consistency in item quality, and faculty perceptions. Design A mixed-methods study using a three-phase design (QUAN + QUAL → quan). Methods Phase 1 compared writing time and item quality between manual and AI-assisted methods from nine nurse educators. Phase 2 conducted cognitive interviews to capture perceptions of usability and value for students and faculty. Phase 3 analyzed survey responses from 250 faculty regarding workload and time management. Results The use of ATI's Claire AI reduced the average writing time from 10.7 to 3.5 min and tripled productivity. Item quality ratings were comparable across the two conditions. Through cognitive interviews, faculty highlighted the benefits such as reducing workload burden and supporting new faculty onboarding. However, faculty expressed the need for expert review of AI-generated distractors. Survey findings verified substantial time savings and growing adoption of AI tools. Conclusion AI-assisted item writing significantly enhances efficiency without compromising quality. Empirical results offer a promising AI strategy in nursing education.
No takes yet. Share an insight, caveat, or question.
Yoo et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: