Discover the Future of Nursing Education: A Scoping Review on AI Integration in Simulation
In an era where artificial intelligence (AI) is rapidly transforming health education, a groundbreaking scoping review titled “Integration of Artificial Intelligence in Nursing Simulation Education” sheds light on the diverse applications and profound impact of AI in preparing the next generation of nurses. Published in Nurse Educator, this comprehensive review, authored by Maggie Mee Kie Chan, Abraham Wai Him Wan, Daphne Sze Ki Cheung, Edmond Pui Hang Choi, Engle Angela Chan, Janelle Yorke, and Lizhen Wang, is essential reading for educators and practitioners alike.
Purpose and Scope: The review meticulously maps AI applications across the three crucial phases of nursing simulation education: prebriefing, simulation, and debriefing. Analyzing 14 peer-reviewed articles published between 2020 and 2024, the study addresses a critical knowledge gap in understanding AI’s implementation in this rapidly evolving field.
Key Findings and AI Applications: The integration of AI offers new opportunities for enhancing clinical competency development and revolutionizing traditional nursing simulation. The review highlights AI’s versatile applications across the simulation continuum:
- Prebriefing Phase: AI, including image-generating tools like Midjourney, demonstrates capabilities in creating comprehensive patient backstories through visual elements and interactive scenarios, which enhances student engagement and reduces anxiety through standardized preparation materials. Additionally, chatbot integration for history-taking instruction offers opportunities for repeated practice in communication skills, providing consistent learning experiences with immediate feedback mechanisms.
- Simulation Phase: This phase demonstrates a vibrant ecosystem of AI platform applications, including Artificial Intelligence Assisted Interactive Screen-Based Simulation (AI-AISBS) software, Google Cloud’s Dialogflow engine, Virtual Counseling Applications (VCAAI), ChatGPT, and SafeBot. These tools effectively facilitate communication skills training through screen-based simulations and enhance learning outcomes through sophisticated feedback mechanisms, such as algorithmic scoring for clinical skills and structured feedback based on standardized interview rating scales. Student engagement is improved through humanoid 3D avatars and voice recognition systems, allowing natural verbal communication with virtual patients. AI-empowered systems also support knowledge construction, promote self-efficacy in interprofessional communication, and aid clinical decision-making in emergency scenarios. Advanced features like Tone Analyzer software objectively evaluate students’ emotional responses. These platforms offer enhanced accessibility through smartphone or desktop devices and significantly reduce faculty workload through automated feedback systems and virtual patient simulations.
- Debriefing Phase: One study examined AI integration in simulation debriefing, implementing a GPT3.5-based Chatbot system combined with Deepgram and Open Pose technologies. This AI-enhanced debriefing demonstrated effectiveness through precise and objective feedback delivery, automated performance analysis, and personalized learning experiences. The integration of ChatGPT-based systems facilitated interactive debriefing sessions, allowing students to seek additional clarification and a deeper understanding of their performance, leading to significant improvements in student confidence, knowledge, skills, and satisfaction compared to traditional debriefing methods.
Benefits and Enabling Factors: AI-enhanced simulations provide a multitude of benefits, including standardized learning experiences through neural networks and personalized learning pathways that adjust to individual student progress and learning styles via Natural Language Processing. They foster enhanced realism, engagement, and personalization in simulations, allowing students to develop clinical skills, critical thinking, and decision-making abilities in a safe environment. AI also offers real-time, personalized, and objective feedback, fosters active participation, critical reflection, and metacognitive thinking through real-time cognitive prompts. Furthermore, AI addresses managerial barriers in clinical nursing education by reducing time and complexity in scenario creation, generating diverse scenarios, and maintaining cost-effectiveness. Students reported decreased stress levels due to non-judgmental AI feedback, increased confidence through repeated practice, and higher engagement through interactive, personalized learning experiences.
Challenges and Future Directions: Despite the immense potential, the review also identifies significant implementation barriers across the simulation continuum. These include:
- Technical Challenges: Faculty’s limited familiarity with AI tools, communication limitations (e.g., processing varied pronunciations, accents, complex sentences), system constraints (e.g., restricted response windows, verbal response latency, inadequate programming capacity for complex scenarios), and issues with system infrastructure and technological compatibility.
- Pedagogical Challenges: The need for structured feedback systems and varying difficulty levels in chatbots, the solitary nature of immersive virtual reality experiences compromising collaborative learning, and student adaptation issues regarding technology acceptance and varying levels of digital literacy.
- Ethical Concerns: Concerns regarding the ethical implications of AI-generated content, data security management, technology dependence, transparency requirements in AI-based assessment processes, equity in AI implementation, and AI bias across diverse populations. Maintaining authentic human elements remains challenging, requiring robust ethical frameworks. Current AI systems may also not fully capture diverse healthcare practices and cultural aspects in nursing education.
Conclusion: This scoping review underscores that while AI holds significant potential for transforming teaching and learning practices in nursing education through improved accessibility, standardization, and personalized learning, its successful integration requires structured faculty support, robust technical infrastructure, continuous quality assurance, and ongoing evaluation. Educational institutions must prioritize infrastructure development and faculty support through integrated AI simulation platforms with user-friendly interfaces, balancing technological advancement with pedagogical effectiveness. Further rigorous research is crucial to establish evidence-based best practices and evaluate the long-term impact on nursing education and clinical practice.
Recommended Citation: Chan, M. M. K., Wan, A. W. H., Cheung, D. S. K., Choi, E. P. H., Chan, E. A., Yorke, J., & Wang, L. (2025). Integration of artificial intelligence in nursing simulation education: A scoping review. Nurse Educator, 50(4), 195–200. https://doi.org/10.1097/NNE.0000000000001851
