Background: Newly graduated nurses often meet competency standards yet struggle to function independently in clinical practice, suggesting that competence alone does not explain readiness for autonomous practice.Purpose: To propose a developmental model explaining the transition from competence to autonomy and its implications for nursing education and workforce transition.Methods: Contemporary literature was integrated with grounded theory findings and prior conceptual work to develop a staged model of clinical transition.Results: The model describes 5 developmental phases supported by 3 interacting mechanisms: mediated clinical experience, professional identity formation, and developmental calibration. Ownership of practice represents the pivotal transition from externally guided performance to internally regulated clinical judgment and responsibility.Conclusion: Transition to autonomy is developmental rather than time-dependent. The model provides a framework for designing clinical education, transition-to-practice programs, and workforce strategies that intentionally support autonomous professional practice.
Background: Simulation-based education is central to nursing curricula but is resource-intensive, particularly when intended to support repeated, low-stakes practice. Artificial intelligence (AI) is increasingly explored as a strategy to extend simulation-based learning.Problem: Traditional simulation models limit learner access to preparatory rehearsal and individualized feedback due to faculty time and infrastructure constraints.Approach: We developed and integrated AI-driven interactive avatars as an adjunct to faculty-led nursing simulation at a large, research-intensive school of nursing. Students were expected to complete avatar-supported asynchronous pre-simulation preparation using structured clinical dialogue, guided feedback, and reflection within a faculty-governed pre-brief-encounter-debrief framework aligned with established simulation standards. This project was implemented for feasibility and acceptability; student learning outcome data are planned for future iterations.Outcomes: Early observations suggest learner engagement, improved preparedness for in-person simulation, and perceived value of low-stakes, repeatable practice. All students completed the avatar assignment and provided feedback on end-of-simulation evaluation forms as well as self-reflections. Anecdotal faculty feedback noted increased student engagement and confidence during the simulation.Conclusions: AI-enabled avatars may enhance access to preparatory simulation experiences while reinforcing, rather than replacing, human-centered nursing education.
Background: Agreement between ChatGPT-assisted and faculty grading of nursing examinations remains uncertain. Purpose: To compare ChatGPT-assisted and faculty grading across item types and examine bias, repeatability, pass–fail classification, and major scoring discrepancies. Methods: We analyzed 506 undergraduate nursing examinations using paired comparisons, intraclass correlation coefficients, error measures, and Bland–Altman analysis. Multiple-choice repeatability and 150 constructed-response scoring units with major overestimations were also examined. Results: Multiple-choice grading showed minimal bias (−0.05 points) and excellent agreement with faculty grading (intraclass correlation coefficient = 0.949). ChatGPT overestimated the constructed-response subtotal and total scores by 7.48 and 7.43 points. Of the 59 examinations with faculty total scores below the institutional passing threshold of 60 points, ChatGPT classified 40 as passing. Major overestimations mainly reflected insufficient deductions and over-crediting of generalized or rubric-external content. Conclusions: ChatGPT showed high agreement for fixed-key multiple-choice scoring but systematically overestimated constructed-response scores. Faculty review remains necessary for consequential assessment decisions.
