Emmanouil Zoulias

National and Kapodistrian University of Athens, Greece

Title: From AI Literacy to Curriculum Design: A Systematic Literature Review of AI Tools for Nursing Education

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Biography

Dr. Emmanouil Zoulias is a member of the Laboratory Teaching Staff at the Health Informatics Laboratory, Department of Nursing, National and Kapodistrian University of Athens. He holds a PhD in Artificial Intelligence and Decision Support Systems, an MSc in Biomedical Technology, and a degree in Electrical and Computer Engineering. His research interests include Artificial Intelligence, Machine Learning, Health Informatics, Data Analytics, IoT, and emerging technologies in healthcare and education. He has extensive experience in IT, public health, research, and higher education, and has participated in numerous European and national research projects. His current research focuses on the application of Artificial Intelligence in Nursing Education.

Abstract

Abstract

The use of artificial intelligence (AI) in healthcare is expanding rapidly, creating increasing pressure to prepare future professionals to engage with AI critically and responsibly. Within undergraduate nursing education, concepts such as AI literacy and digital readiness are frequently discussed, yet educators still lack a practical, pedagogically structured overview of specific AI tools aligned with learning goals. This gap makes it difficult to integrate AI into teaching, learning activities, and assessment in a systematic way.

This study addresses the question: Which AI tools can be used in nursing programmes, and how can they effectively support student learning? A systematic literature review was conducted to identify and analyse published studies on AI tools relevant to nursing education and nursing informatics. The review examined studies retrieved from selected academic databases using predefined search terms related to artificial intelligence, nursing education, and healthcare training. Inclusion and exclusion criteria were applied to select studies focusing on higher education, nursing or closely related health professions education, and concrete AI-supported educational applications. Data was analysed using a qualitative thematic synthesis to identify recurring tool types, pedagogical functions, and curriculum-relevant patterns.

The synthesis identified five main categories of AI tools relevant to nursing education: (1) generative AI chatbots and virtual tutors, (2) AI-assisted literature review and academic support tools, (3) adaptive and personalised learning platforms, (4) AI-enhanced virtual patients and clinical simulators, and (5) extended reality applications supported by AI. Across the literature, these tools were associated with several recurring educational functions, including support for knowledge acquisition, clinical reasoning, feedback, decision-making practice, simulation-based learning, and learner personalisation. The review also found that successful educational use depends not only on technological availability but on alignment with intended learning outcomes, faculty guidance, and explicit attention to ethical and critical use.

The study provides a structured catalogue of AI tools and an initial mapping of tool categories to educational purposes in nursing curricula. Rather than treating AI as an occasional add-on, the findings support a more coherent curriculum strategy in which AI tools are deliberately selected, critically evaluated, and pedagogically justified. These findings may inform educators designing AI-enhanced nursing and health professions programmes, as well as policymakers seeking concrete approaches to responsible AI integration in higher education.