International Research Journal of Education and Technology

In the rapidly evolving landscape of healthcare, nursing education faces mounting pressures: shrinking clinical placement opportunities, increasing complexity of patient care, and the need to prepare nurses who can adapt to change, think critically, and practice evidence-based care. Traditional, “one-size-fits-all” lecture formats are less able to address the heterogeneous needs of learners, especially when student cohorts vary widely in prior knowledge, learning pace, and learning style. In response, many educators and institutions are exploring AI-driven adaptive learning platforms — systems that use algorithms and data analytics to dynamically adjust content, pacing, and feedback in response to learner performance.

Such platforms promise to offer personalized learning pathways, detect knowledge or skill gaps in real time, deliver timely feedback, and generate customized study plans tailored to each student’s needs. In the context of nursing education — where mastery of complex, often procedural, clinical skills is critical — these AI-enabled systems have especially strong potential. However, adoption is not without challenges: issues of transparency, bias, infrastructure, scalability, human oversight, and pedagogical alignment remain.

This article presents a conceptual framework for AI-driven adaptive learning in nursing education; reviews existing evidence and use cases; discusses benefits and limitations; and offers recommendations for educators, institutions, and researchers.