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Nursing Education

created 2026-09-17 · updated 2026-09-18

Nursing Education — the preparation of nurses for licensed practice, and the subfield of health-professions education where AI is currently most studied. AI enters nursing curricula through Simulation with virtual patients and mannequins, LLM-based study and clinical-reasoning support, automated assessment, and adaptive platforms for at-risk learners. Its object is distinctive: nursing competence fuses psychomotor skill, relational practice, and professional identity formation, so a technology that raises measured performance can simultaneously erode the developmental work that produces a nurse.

Questions to Consider

  • AI-supported simulation reliably improves knowledge and Self-Efficacy but shows inconsistent, sometimes negative effects on complex psychomotor skill. Where is AI the right teacher, and where must a human body in the room remain non-negotiable?
  • Nursing students describe AI as emotional comfort during clinical stress. Is that a support to design for, or a signal that the relational apprenticeship is under-resourced?
  • AI anxiety among health-sciences students tracks job-search anxiety. Is teaching AI literacy the remedy, or does it raise the salience of a threat that institutional policy should address instead?

Introduction

Nursing education is the clinical strand of the knowledge base with the densest empirical record: system-wide reviews of AI applications, a mixed-methods synthesis of AI-powered simulation, a qualitative study of student and faculty experience, and a critical integrative review of LLM research sit alongside one another. That makes nursing a useful test case for claims the wider AI in education field makes about clinical training.

It must be kept distinct from Medical and Health Professions Education, which treats nursing as one program among medicine, pharmacy, dentistry, and allied health and organizes its account around the shared problem of scalable clinical competence. The nursing literature adds three things that page cannot carry: a competence boundary at which AI's documented benefits stop; an explicitly professional-identity framing, in which the question is who becomes a nurse rather than what a trainee can do; and a workforce layer — students' anxiety about AI displacing nursing roles, and the faculty view of nurses as shapers rather than recipients of healthcare's digital transformation.

The field also refines three adjacent concepts. From Simulation it inherits the fidelity debate but renames the deficit an "authenticity gap." From Affective Computing it takes the measurement of affect, yet its strongest claim is that affective outcomes improve while emotional depth in the interaction does not. From Self-Efficacy it takes the central mediating variable, while cautioning that confidence built in a low-risk setting may not transfer to the clinical one. Beneath all of it sits the ordinary higher education problem of workload, access, and assessment integrity, sharpened by licensure and patient consequence.

How AI appears in nursing education

  • Four application areas, recurring risks. Alrazeeni et al. (2026) reviewed 28 empirical studies (2010–April 2025) and group AI applications into personalized learning, simulation-based training, Automated Assessment, and institutional curriculum management with predictive analytics. Technological inequity, faculty preparedness gaps, and privacy and bias concerns recur. Their recommendations are concrete — embed AI simulation in emergency-care training, deploy adaptive platforms for at-risk learners, use automated tools for real-time formative Feedback, adopt diagnostic accuracy as the impact measure — within 6–12 month multi-site pilots tracking learning outcomes and trust.
  • Simulation: strong on knowledge and confidence, conditional on skill. Jiang et al. (2026)'s PRISMA-guided mixed-methods review of 19 studies (N = 1,253, mostly prelicensure students) covers generative AI/LLMs, AI-driven virtual patients and mannequins, AI-enhanced VR/mixed reality, and chatbots. The strongest designs — three RCTs plus controlled quasi-experiments — show significant gains in cognitive knowledge and affective outcomes including Self-Efficacy and communication confidence, but effects on complex psychomotor skills are inconsistent, and one RCT found AI-assisted simulation inferior to standardized-patient simulation. Qualitative meta-aggregation explains both the appeal and the limit: learners value safe, repeatable, nonjudgmental practice that bridges the theory–practice gap, but report an authenticity gap — robotic dialogue, missing nonverbal cues, no tactile examination — plus technical instability that raises extraneous cognitive load and state anxiety. The authors recommend a stepped continuum: AI for pre-learning, history-taking, and foundational reasoning; human simulators and standardized patients for complex psychomotor and emotionally loaded work; human-facilitated debriefing alongside AI feedback.
  • Acceptance is real, and relational. Akbaba and Calik Kus (2026) interviewed 28 participants (16 students, 12 faculty) and analyzed transcripts deductively through the Technology Acceptance Model. TAM held: ease of use, usefulness, intention, and use shaped adoption. The role split is practical — students used AI for presentations, visual content, clinical case analysis, and care planning; faculty for course materials, literature review, academic writing, and administration — implying tailored rather than uniform training. Two findings extend TAM's cognitive frame. Participants described AI as psychosocial support, a "companion" offering reassurance and a confidential space to reflect during clinical stress. And where AI scored higher than nurses on empathy in a high-volume context, Sun et al. (2026) read it as Structural Empathy Suppression: overwork makes authentic empathic expression unsustainable, algorithmic consistency fills the gap, and identity-constituting significance transfers to the algorithm — reframing the policy question from "how effective is this tool?" to "what conditions made it appear necessary?"
  • Faculty read the risk as professional, not procedural. Dabkowski et al. (2026) interviewed 22 nursing academics across Australia and New Zealand and found them navigating policy that was absent, late, or written without them, sharply divergent collegial attitudes, and a line drawn at replacement rather than use. The objection was developmental before it was procedural: generating answers for a deteriorating-patient scenario means the reasoning is never practised, and participants warned of a future cohort not fit for practice — "Copilot won't teach you to be a nurse." Assessment practice was already moving toward invigilated exams, oral vivas and process evidence, and misconduct was read as a rehearsal for unsafe clinical shortcuts, which reframes Academic Integrity as a question about who is safe to practise rather than a compliance process. The academics asked for nursing-specific guidance tied to the profession's ethical codes, GenAI literacy embedded across the curriculum, and staff development with unit co-design — not blanket prohibition.
  • Anxiety as a workforce signal. Dağ et al. (2026) surveyed 821 health-sciences students (nursing among them) and found a moderate positive correlation between AI anxiety and job-search anxiety (r = 0.233, p < 0.001), with AI anxiety a significant predictor after controlling for socio-demographics (β = 0.234, p < 0.001). AI anxiety, they argue, is not a technology attitude but a psychological factor shaping how students see their professional futures — pointing to AI Literacy and career counseling, not tooling, as the intervention.
  • Groupthink and interprofessional teams. Wiss et al. (2025) placed a generative AI agent (CALIE) into twelve newly formed interprofessional teams across seven health-professions programs, including nursing, during a 180-minute virtual problem-based learning session, prompting it to inject controversial viewpoints. Of 165 learners, 158 completed the survey; the agent was rated most useful as a tool (M = 3.49), then for feedback helpfulness (M = 3.21), and least as part of the team (M = 2.88), all pairwise differences significant (F(2, 156) = 26.01, p < .001). Facilitator stance mattered, and learners who rejected the agent's responses still used them as a socially permissible way to speak up — disagreement with the AI did team work.

Competence, identity, and the evidence gap

  • Displacement is the design variable. Sun et al. (2026)'s critical integrative review of 489 studies across 47 countries reframes LLM research in nursing around one criterion: whether the cognitive and participatory work an LLM displaces is extraneous to or constitutive of the competence being developed. Displacing documentation and routine retrieval frees working memory; displacing a full reasoning chain, an ethical justification, or an individualized care plan removes the object of learning. The evidence is not hypothetical — students using ChatGPT as a sole resource scored significantly below textbook controls on ethical standards and clinical reasoning, and an AI-integrated curriculum produced higher scores alongside less individualized, weaker-logic care plans, a performance–learning dissociation. Their Professional Identity Tension Model formalizes this across task, competency, and identity layers: outcomes measured with a tool available may index fluent performance rather than durable capability, so programs should use delayed, no-tool post-tests and transfer tasks.
  • The evidence is inverted. The same review's gap map finds no randomized or quasi-experimental study of professional identity or career development, only three of relational and ethical competency, and no follow-up beyond 12 months — while controlled evidence clusters in cognitive and technical outcomes.
  • Surveillance is documented as perception, not as improved conduct. Harerimana et al. (2026) mapped Remote Proctoring in nursing assessment across six studies (1,567 students) and found deterrence reported almost universally by proctored students (98–100% agreement in one graduate programme), while the single comparative performance study found an in-person proctored cohort scoring significantly higher on a readiness exam than a remotely proctored one. Anxiety ran both ways — some students calmer at home and relieved of travel, others unable to concentrate while watched and fearful of wrongful accusation — and four of the six studies reported connectivity failure, one load shedding, making equitable access the operative constraint rather than exam security. The authors' position is that remote proctoring should be one instrument among several, governed by data-protection standards and paired with integrity-by-design and authentic assessment rather than treated as the default safeguard.
  • Boundary conditions. The strands converge on the same restraint. AI is best evidenced for structured, repeatable objectives — foundational communication, history-taking, health education, knowledge acquisition — and least evidenced for complex psychomotor skill, emotionally loaded interaction, and long-term professional formation. Acceptance is consistently moderate to high, but acceptance of AI feedback is conditioned by trust: learners prefer human sources perceived as benevolent over AI perceived as merely competent. The reviews are also geographically narrow, dominated by uncontrolled designs and self-report, and — in the simulation review — show a near-uniform positive pattern that raises publication-bias concerns.

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Metadata

DisciplineNursing Education, Medical and Health Professions Education
LevelHigher Education
Intended audienceMedical Educators, Teaching, Curriculum Designers, Teaching, Researchers
Learning and instructionSelf-Efficacy
Technologies and techniquesGenerative AI, Simulation
PeopleWorkplace Learning
Equity, ethics, and responsible useEquity
Foundations of AI in educationLearner Identity
Page kindSynthesis
SourceHistoryEditChat

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