Commentary: Integrating artificial intelligence into healthcare professionals education — a balanced perspective

Summary of the paper

The review aimed to evaluate whether AI-based and technology-based educational tools improve learning outcomes compared to conventional teaching methods. Following, Prefereed Reporting Items for Systematic Reviews and Meta-analyses(PRISMA) guidelines, Amandu Matua et al. (2026) explored six databases, included only randomised controlled trials published between 2015 and 2025, and the authors analysed nine studies conducted across multiple countries and healthcare disciplines. The interventions examined included virtual simulations, AI-supported problem-based learning, ChatGPT-assisted teaching, adaptive learning platforms and AI-driven feedback systems.

The findings suggest that AI-enhanced educational approaches offer notable benefits in certain domains. In particular, improvements were observed in diagnostic accuracy, long-term knowledge retention and, in some cases, clinical judgement and learner satisfaction. Tools such as virtual simulation and AI-generated personalised feedback enabled interactive and adaptive learning experiences, allowing learners to engage with clinical scenarios in a safe and controlled environment. AI-based educational tools are beneficial to the lecturers as it enhances teaching efficiency, improves assessment quality, provides real-time learning analytics and saves time in administrative tasks.

However, the review also highlights that AI-based methods do not consistently outperform traditional teaching. Outcomes related to critical thinking, clinical decision-making and skill performance were often comparable between AI-supported and conventional approaches. Similarly, evidence regarding time efficiency and learner satisfaction was mixed. Overall, the authors conclude that AI should be viewed as a complementary educational tool rather than a replacement for traditional teaching methods.

Connection to nursing theory, practice and research

The findings of this review resonate strongly with contemporary nursing education frameworks that emphasise experiential and learner-centred approaches. According to Benner’s Novice to Expert model (Benner, 1984), the incorporation of AI-based tools, especially simulation and adaptive learning platforms, can facilitate the transition from novice to competent practitioner by offering opportunities for reflective practice and repeated exposure to clinical scenarios. In clinical teaching settings, similar trends have been observed, where students show greater engagement, enhanced clinical competence, and increased confidence when exposed to simulation-based learning and digital tools. The interactive nature of AI-supported platforms aligns with adult learning principles, where active participation and immediate feedback enhance knowledge retention (Khakpaki, 2025; Wei et al., 2025).

At the same time, the review’s finding that traditional methods remain equally effective in certain domains is particularly important. Nursing, as a profession, is deeply rooted in human interaction, empathy and ethical decision-making. These aspects are difficult to replicate fully through AI-based systems. Clinical reasoning often involves ambiguity, contextual judgement and emotional intelligence – skills that are best developed through bedside teaching and discussions. This aligns with broader literature highlighting that although AI can support cognitive and technical skill development, it may be less effective in addressing the affective and ethical dimensions of care (Bozkurt et al., 2025).

Interestingly, the mixed findings regarding learner satisfaction also reflect real-world experiences. Although some students appreciate the flexibility and interactivity of AI tools, others may find them impersonal or challenging to navigate without adequate guidance. This suggests that the success of AI integration depends not only on the technology itself but also on how it is implemented within the curriculum.

Practical implications: who can benefit from this research?

Nurses are at the forefront of patient care, and the integration of AI into nursing education and practice represents a paradigm shift with the potential to substantially improve healthcare delivery. AI-based tools offer significant complementary values but should be integrated to enhance-not-replace traditional teaching methods in health professions education (Wei, Dai et al., 2025).

For nurse educators, the findings support the strategic integration of AI-based tools to enhance teaching effectiveness. AI-enabled simulations, virtual patients and adaptive learning platforms can be particularly useful for teaching complex clinical concepts, improving diagnostic reasoning and reinforcing long-term retention. However, educators should adopt a blended approach, combining AI tools with traditional teaching methods such as case discussions, clinical placements and reflective debriefing. Such an approach ensures that both technical competence and humanistic aspects of nursing care are addressed.

For nursing students, AI-based learning tools offer opportunities for self-directed learning and skill development at an individual pace. The ability to receive immediate, personalised feedback can help students identify learning gaps and improve performance. This is especially valuable in resource-constrained settings where access to faculty or clinical exposure may be limited.

For clinical practitioners and healthcare organisations, the findings highlight the potential of AI-supported training in continuing professional development. Simulation-based and AI-assisted learning can enhance clinical competence, standardise training and improve patient safety. These tools may be particularly beneficial in high-risk or specialised areas such as critical care, emergency nursing and diagnostic imaging.

From a policy perspective, policymakers and regulatory bodies should develop guidelines to ensure ethical use, data privacy and quality assurance in AI-assisted learning. Investment in faculty training and digital infrastructure is also essential to maximise the benefits of these technologies.

Conclusion

This systematic review provides a balanced perception on the evolving role of artificial intelligence in health professions education. For nursing education, the key lies in achieving a thoughtful balance – leveraging the strengths of AI to enhance learning while preserving the human-centred values that define the profession. Overall, the results show that AI-based teaching modalities should be seen as a supplemental tool to enhance healthcare professionals’ and students’ education through careful integration and application rather than as a substitute for conventional teaching methods. Ultimately, the integration of AI should not be driven by technological enthusiasm alone, but by a clear focus on improving educational quality, clinical competence and patient care outcomes.