Building Artificial Intelligence Literacy in Nursing Education for Ethical and Clinical Practice

The article proposes a structured, three-year curriculum framework developed at Deakin University to embed critical artificial intelligence literacy into pre-registration nursing education, ensuring students are prepared for AI-enabled healthcare. It emphasises ethical understanding, critical evaluation, and the balanced use of AI alongside human judgment to support safe and responsible nursing practice.

An article, written by researchers from the School of Nursing and Midwifery and the Centre for Quality and Patient Safety Research at the Institute for Health Transformation, Deakin University (Australia), responds to a growing challenge in nursing education: how to prepare future nurses for a healthcare system increasingly shaped by artificial intelligence. Since the public release of generative AI tools such as ChatGPT in late 2022, universities have faced concerns about academic integrity and learning authenticity, while healthcare systems have simultaneously accelerated their use of AI for clinical decision-making. Nursing education sits at the intersection of these trends, requiring programs to both manage AI use in learning and ensure graduates are ready to work safely and ethically with AI in clinical practice.

Artificial Intelligence in Modern Healthcare

The authors outline how artificial intelligence is already embedded across healthcare. AI systems support medical imaging, disease prediction, patient monitoring, and administrative efficiency, while generative AI tools are increasingly used for patient education, communication, and chronic disease management. In nursing and midwifery, AI is being applied to predict risks such as falls, pressure injuries, sepsis, and hospital readmissions, as well as to support discharge planning and care coordination. While these tools offer clear benefits, they also carry risks related to bias, data quality, transparency, and over-reliance. The authors emphasise that nurses must remain accountable for care decisions and cannot simply defer judgment to algorithms.

The Need for Critical AI Literacy

A central argument of the paper is that nurses need more than basic technical skills to use AI effectively. The authors define “critical artificial intelligence literacy” as the ability to understand how AI systems work, recognise their limitations and biases, and reflect on their ethical, social, and professional implications. Regulatory bodies already expect nurses to apply human judgment, protect patient privacy, and obtain informed consent when AI is used in care. However, the authors note that while professional guidance exists for practice, there are no clear standards for how AI literacy should be taught within nursing curricula. This gap risks inconsistent education and leaves both students and educators uncertain about expectations.

A Three-Year Scaffolded Curriculum Framework

To address this gap, the authors present a structured curriculum framework that embeds AI literacy across a three-year pre-registration nursing degree. Developed after a pilot program at Deakin University, the framework is based on scaffolded learning, meaning students build knowledge gradually through increasing levels of complexity. In the first year, students are introduced to basic AI concepts in an academic context. The focus is on understanding generative AI tools used in higher education, learning how they can support study, and recognising ethical boundaries such as plagiarism and responsible use.

In the second year, the emphasis shifts to healthcare applications. Students engage with clinical case studies, simulated patient interactions, and introductory AI tools used in health settings. They are encouraged to analyse ethical issues, identify bias in AI-generated outputs, and assess how AI can both support and limit nursing practice. Hands-on engagement is prioritised to help students move beyond theory.

By the third year, students focus on real-world clinical application. They critically evaluate AI-generated data, use AI tools to support care planning for complex patients, and reflect on how AI insights should be balanced with professional judgment. The goal is to prepare graduates who can use AI confidently while maintaining autonomy, accountability, and ethical responsibility.

Implications and Future Directions

The authors argue that this framework offers a practical and flexible way to prepare nursing students for an AI-enabled healthcare system. It is not intended to be prescriptive but adaptable to different institutional and clinical contexts. They acknowledge challenges, including uneven student engagement and concerns about over-reliance on AI, as well as unresolved issues around assessment design in the age of generative AI. Nevertheless, they conclude that embedding AI literacy transparently and progressively is preferable to restrictive approaches. By developing informed, reflective, and critical users of AI, nursing education can ensure that future nurses are not passive recipients of technology but active professionals capable of integrating AI responsibly into patient care.