BMJ Open Protocol Maps Artificial Intelligence in Nursing Leadership Training
Artificial intelligence is moving into classrooms, simulation labs, and clinical decision support systems faster than nursing curricula can usually adapt. A new BMJ Open scoping review protocol sets out to examine how AI is being used in nursing leadership education, with attention to both undergraduate and graduate training. The project is designed to chart what has been studied, how leadership has been taught, and where the educational evidence remains thin.
The protocol is timely for a field in which nurses are expected to lead teams, coordinate care, interpret data, and respond to rapidly changing technologies. Leadership training is no longer limited to communication exercises or administrative theory. Nursing students now encounter digital tools that can sort information, generate recommendations, and assist with workflow decisions, which means leadership education has to prepare learners for both human and machine-mediated care environments.
One sentence captures the problem plainly: nursing education has not settled how to teach leadership when artificial intelligence is part of the clinical backdrop.
Leadership Competence Meets Algorithmic Care
The review focuses on a practical question with growing relevance across training programs: how is AI being incorporated into the teaching of leadership skills for nurses? In the nursing context, leadership is not a vague professional trait. It includes decision-making, delegation, prioritization, collaboration, ethical judgment, and the ability to respond when care systems become more complex. Artificial intelligence touches each of those domains, sometimes by supporting judgment and sometimes by introducing new uncertainty.
AI itself is defined in the protocol as technology that enables computers to perform tasks such as learning, decision-making, and reasoning that would ordinarily require human intelligence. In clinical education, that definition matters because leadership instruction has to go beyond technical literacy. Students need to understand how automated tools may influence triage, documentation, communication, and team coordination, especially when machine outputs are treated as if they were neutral or definitive.
The review will cover both undergraduate and graduate settings. That breadth is important because leadership development in nursing does not begin at the point of licensure. Undergraduate learners are often building foundational habits of delegation and communication, while graduate students may already be moving into more advanced managerial, educational, or systems-based roles. A single curriculum model is unlikely to fit both groups, and the protocol is structured to reflect that range.
Why a Scoping Review Fits the Question
A scoping review is an appropriate design when a field is emerging, definitions are inconsistent, and the existing literature is likely to be diverse in method and scope. Here, the team is not attempting to produce a narrow effectiveness estimate or a pooled intervention effect. Instead, the goal is to map the evidence base and identify how researchers have framed the relationship between AI and nursing leadership learning.
Quality appraisal will not be performed, which is standard for a scoping review and aligns with the aim of breadth rather than formal evidence grading. The absence of quality assessment also means the final synthesis will be descriptive rather than judgmental, emphasizing what has been published and where the gaps lie. For education research in a fast-moving technological area, that approach can be more useful than forcing heterogeneous studies into a single evaluative mold.
One limitation is already visible in the protocol: leadership itself lacks a universally accepted definition. In a literature review, that kind of conceptual drift can complicate search strategies and make study synthesis less precise. Some investigations may frame leadership as team management, others as clinical influence, and others as organizational decision-making. Without a shared definition, studies can appear related while actually examining different educational targets.
Canadian Nurse Educators Shape the Review Framework
The study team includes nurse educators from across Canada, bringing together experience in teaching, curriculum design, and leadership development. That multidisciplinary educational background should matter in the final synthesis, because the question is not only whether AI is present in nursing education, but how it is used pedagogically. A curriculum-focused lens can distinguish between a simple mention of AI and a structured learning strategy that genuinely supports leadership competence.
This matters at both ends of the training spectrum. In undergraduate education, AI may be introduced as a conceptual tool to prompt discussion about ethics, bias, or decision support. In graduate education, it may be tied to more sophisticated leadership tasks such as resource allocation, quality improvement, or workforce planning. The protocol leaves room for those differences and appears designed to capture the educational roles AI may play rather than reducing all uses to a single category.
The review will likely be useful for educators who are trying to decide whether AI should be treated as content, method, or both. Each option has different implications. If AI is taught as content, students learn about the tool itself. If it is used as a method, it may help simulate leadership dilemmas, generate case scenarios, or support reflective analysis. Those are not interchangeable approaches, and a scoping synthesis can clarify how frequently each appears in the literature.
What the Final Synthesis Will Need to Clarify
Because this is a protocol, no results are available yet. The value lies in what the final review may reveal about the shape of the literature: whether most studies are conceptual, whether educational interventions are being tested, and whether leadership outcomes are being measured in any consistent way. The protocol also raises the question of whether current programs are preparing nurses for the ethical and operational consequences of AI-assisted care.
The practical implication is straightforward. If AI is already altering clinical workflows, then nursing leadership education cannot stay anchored to pre-digital models of authority and supervision. Leadership preparation has to account for data-driven systems, automated recommendations, and the possibility that technology will influence how nurses exercise judgment at the bedside and across care teams.
The review’s final usefulness will depend on how clearly it can sort definitions, educational levels, and intervention types. A field with mixed terminology can generate a large but shallow literature. A careful scoping review can turn that noise into a usable map, even when the evidence cannot yet support stronger claims about effectiveness or best practice.
For now, the protocol establishes the boundaries of a question nursing schools are only beginning to answer: how should leadership be taught when artificial intelligence is becoming part of the professional environment nurses will have to manage?