Driven by the American Association of Colleges of Nursing’s Essentials: Core Competencies for Professional Nursing Education, nursing programs are moving away from time-based models toward competency-based education (CBE), where students progress based on demonstrated skills rather than seat time.
At the same time, rapid advances in artificial intelligence and virtual reality are reshaping how those competencies are taught and assessed.
For deans and faculty, the shift is no longer theoretical. It is increasingly reflected in budget lines for virtual reality headsets, AI-enabled simulation platforms, and faculty development workshops, as well as in questions from university leaders about how these investments position nursing programs in an increasingly competitive market.
CBE as the New Baseline
AACN defines competency-based education as “a system of instruction, assessment, feedback, self-reflection, and academic reporting” organized around what students can demonstrate—knowledge, skills, attitudes, and professional behaviors—rather than how long they spend in class.
The revised Essentials framework outlines 10 domains and explicit competencies expected across the trajectory of nursing education, from baccalaureate through advanced practice.
The goal is to close the growing gap between academic success and practice readiness.
In summaries of the framework, AACN describes the shift as moving from a “knowing” paradigm to a “doing” paradigm that emphasizes measurable, real-world performance.
The National League for Nursing has echoed that vision, encouraging schools to integrate competency-based models that ensure graduates are prepared not only to know, but to do.
This outcomes-based approach has turned simulation, informatics, and assessment technologies from optional enhancements into core infrastructure for meeting national standards.
VR and Simulation
Virtual reality has become one of the most visible technologies supporting the transition to competency-based nursing education.
A growing body of research suggests that VR simulation can equal or outperform traditional instruction for specific clinical skills and knowledge outcomes.
A 2023 systematic review in BMC Medical Education found that VR significantly improved nursing students’ theoretical knowledge, practical skills, and learning satisfaction compared with conventional teaching methods, even as gains in critical thinking were more mixed.
Other studies have shown that VR-based instruction improves infection control knowledge and self-efficacy among undergraduate nursing students.
In some contexts, immersive VR has performed as well as—or better than—traditional clinical placements.
At George Mason University, a randomized study of students learning acute pediatric care found that those trained in immersive VR outperformed peers trained in inpatient clinical settings.
For competency-based programs, these findings matter because VR experiences can be mapped directly to specific AACN Essentials domains and repeated until mastery is demonstrated.
Recent research also explores how AI-enhanced virtual simulations can further personalize practice. A 2024 study of nurse practitioner students found that VR simulations paired with AI-driven virtual patients and feedback supported clinical reasoning and communication skills when aligned to competency frameworks.
AI in Classrooms and Clinics
Beyond simulation, AI tools are appearing across nursing curricula, from didactic coursework to clinical education.
A 2025 study in BMC Medical Education examined nursing students’ use of generative AI tools such as ChatGPT, Bard, and Bing AI.
Students reported using these tools to clarify complex concepts, generate practice questions, and prepare for assessments, while also expressing concerns about accuracy, ethics, and academic integrity.
In clinical education, researchers are examining AI systems designed to support decision-making and feedback rather than replace instructors.
A 2025 article on AI integration in nursing clinical education described applications ranging from predictive analytics to automated feedback on clinical documentation.
The authors argued that AI has the potential to augment supervision and free faculty to focus on higher-level mentoring, provided educators receive adequate training in AI literacy and ethics.
Across health professions, researchers are also studying large language models as tools for assessment.
A 2024 study of medical Objective Structured Clinical Examinations found that language models could rate communication skills and generate narrative feedback with consistency approaching that of human assessors.
While still emerging, this work suggests possible applications in nursing communication, patient education, and handoff competencies.
Video-Language Models and Automated Skills Assessment
One of the most experimental frontiers involves vision- and video-language models used to assess procedural skills from recorded video.
In 2025, researchers introduced a video-language framework designed to automate assessment of nursing skills.
The system analyzes performance videos, breaks procedures into fine-grained steps, flags missing or incorrect actions, and generates explainable feedback.
The study suggests such tools could deliver scalable, consistent formative feedback while reducing instructor workload—closely aligning with competency-based education’s emphasis on repeated demonstration and transparent criteria.
Broader surveys of medical vision-language models point to rapid advances in tools that combine visual and textual data for training and evaluation.
Although most have not yet been adopted widely in nursing education, they signal a future in which simulation centers pair skills labs with automated video review systems.
Strategic Decisions and Faculty Development
For deans, program directors, and provosts, the convergence of competency-based education, AI, and VR is as much a strategic planning issue as a pedagogical one.
Implementing the AACN Essentials requires institutions to revisit outcomes, assessment plans, and clinical partnerships, often under tight timelines and regulatory scrutiny.
At the same time, developing or upgrading simulation centers, licensing VR platforms, and piloting AI tools can require significant upfront investment.
Faculty development is a recurring theme in the literature.
AACN’s Essentials Toolkit emphasizes resources to help faculty design competency-based curricula and assessments, and to integrate new technologies in alignment with the framework.
Nursing education updates consistently highlight the need for ongoing training so faculty can use simulation, dashboards, and AI tools to support competency development rather than simply adding content.
AI literacy is increasingly framed as a core competency for both faculty and students.
A 2025 guide for nurses on AI notes that clinicians and educators need baseline understanding of data quality, bias, and regulation to participate meaningfully in tool selection and evaluation.
Positioning for the Next Decade
As health systems adopt AI-enabled tools and advanced simulation, nursing programs that demonstrate robust, competency-based preparation may gain an edge in recruiting students, clinical partners, and employers.
VR and AI-integrated simulation offer predictable, standardized learning environments when clinical placements are limited.
AI-supported assessment can help programs document how students meet specific competencies, which is increasingly important for accreditation and accountability.
At the same time, educators caution that technology cannot replace human mentoring, reflective practice, or attention to equity and ethics.
AACN’s Essentials emphasize that competencies such as compassionate care, communication, and understanding social determinants of health must remain central as informatics and health technologies become more prominent.
For academic leaders, the challenge is aligning investments in AI and VR with competency-based frameworks while supporting faculty through curricular redesign.
In that sense, the shift toward smart technology in nursing education is less about tools and more about redesigning learning around performance, feedback, and readiness—with emerging technologies serving as powerful, if still evolving, means to that end.
