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Introduction
Artificial Intelligence (AI) continues to advance and integrate into healthcare activities1, and the emergence of generative AI (GAI) now promises significant implications for education. In this editorial, potential applications of this technology will be explored, with predictions proffered on how its unique abilities in content creation, scenario simulation, and personalised learning experiences might transform healthcare education.

GAI refers to a subset of artificial intelligence technologies that generate new content, from written text to synthetic images, videos and simulations, based on patterns learned from large datasets. Unlike traditional AI, which typically analyses data and provides decisions or classifications, GAI can create novel data outputs that mimic real-world artifacts and scenarios.

Potential benefits
While traditional AI models might recommend learning activities based on learner’s historical data or automate routine tasks, GAI has potential to offer more diverse and innovative learning opportunities to nursing and midwifery education, notably:

  • enhanced simulation and training by generating realistic and complex patient care scenarios, providing nursing and midwifery students with the opportunity to practise and hone their skills in a safe, controlled environment. This includes simulating emergencies, complex or rare conditions, or ethical dilemmas, which are invaluable for developing critical thinking and decision-making skills.
  • personalised education by analysing individual learning patterns and outcomes and tailoring educational content to the needs of each student2. It can adjust complexity of scenarios, or provide timely feedback, additional resources and tutorials based on a learner’s performance and progress, thereby supporting a more effective and personalised learning experience.
  • scalable learning solutions by creating educational modules and materials that can be scaled and distributed across different institutions and geographies. This is particularly beneficial for reaching students in remote or underserved areas, thereby democratising access to quality education and training.
  • supporting educators and enhancing curriculum relevance by assisting educators through automating routine tasks like grading and preparing educational materials, while also providing insights into student performance and learning trends3. Additionally, GAI can aid in the review of educational content by gathering and suggesting the latest research and guidelines.

Future GAI scenarios
Although there has not been a commercialised solution that delivers healthcare education with GAI, it offers significant opportunities to revolutionise education in nursing, midwifery, and broader health fields4. It is envisaged that GAI will soon demonstrate its powerful capabilities through the following innovative scenarios.

Integration with existing technologies
Virtual reality (VR) and augmented reality (AR) have become more prevalent in biomedical and healthcare curricula,5. Combining GAI with VR and AR technologies will revolutionise clinical education by offering students immersive, realistic patient care simulations. Imagine GAI grasping and applying the principles of physics and knowledge of clinical practices and creating diverse and lifelike patient emergencies in a simulation while VR and AR facilitate students interacting with virtual patients and clinical equipment6. This integration enables dynamic scenario generation, multisensory learning experiences, and collaborative learning environments. Moreover, integration of GAI complements existing educational methodologies by enabling more consistent and objective assessments through its advanced data analysis capabilities, and by offering personalised feedback based on individual performance metrics. Such features strengthen clinical decision making skills within a safe and accessible virtual environment. This advanced approach builds upon traditional hands-on practice, introducing a new layer of adaptability to effectively meet diverse individual learning needs.

Personalised learning platforms:
GAI empowers personalised learning paths by analysing learners’ preferences, progress, and performance, and tailoring educational content to their learning pace, style, and needs2. Through advanced algorithms, GAI can track learners’ interactions with educational materials, identifying patterns in their comprehension, retention, and areas of struggle7. For example, if a student struggles with paediatric nursing concepts, GAI could create additional targeted tutorials and quizzes specifically designed to address these weaknesses, complete with virtual patient scenarios that allow for practical application. By understanding each learner’s strengths and weaknesses, GAI models can dynamically adjust the difficulty level of content, provide additional explanations or resources where needed, and offer targeted interventions to address learning gaps8. This adaptive approach ensures learners receive content that is optimised for their specific requirements, enhancing their understanding and retention of complex concepts. Personalised learning paths foster motivation and engagement by presenting content that resonates with each learner’s preferences and interests, ultimately facilitating more effective and efficient learning outcomes.

Challenges and ethical considerations
The previous editorial addressing AI highlighted the dual nature of AI in enhancing care assessment and introducing challenges1. Similar challenges must also be addressed when introducing GAI into educational curricula and preparing students to navigate GAI complexities in their professional roles.

Data privacy and security:
Safeguarding sensitive information in AI-driven personalised learning experiences is crucial to upholding privacy rights, preventing data breaches, and maintaining trust between users and educational institutions or technology providers9. With personal data at the core of these systems, strict measures must be implemented to ensure individuals’ confidential information remains protected from unauthorised access or misuse. This entails compliance with relevant regulations, as well as ethical considerations regarding data usage, consent, and transparency. Robust data protection mechanisms, including encryption, access controls, and regular security audits, are essential to mitigate risks and uphold the integrity of personalised learning platforms, thereby fostering a safe and trustworthy educational environment.

Bias and fairness:
Addressing potential for bias in AI-generated content is fundamental, especially in educational materials used in healthcare settings where accuracy and fairness are paramount. AI systems can inadvertently perpetuate biases that are present in their training data. This can result in educational content that unfairly emphasises or neglects certain information, creating disparities that could misinform students and potentially impact their clinical decision making. For example, if AI is trained on data primarily from adult patients, its generated content might not adequately cover paediatric or geriatric conditions. This is particularly concerning in healthcare, where biased information could contribute to disparities in patient care outcomes10.

Ensuring educational materials are fair, accurate, and representative of diverse healthcare populations is essential for providing equitable learning experiences and promoting inclusive healthcare practices. Steps to mitigate bias include thoroughly evaluating training data for representativeness, diversity, and balance, as well as implementing bias detection and mitigation techniques within AI algorithms11. Moreover, ongoing monitoring and evaluation of AI-generated content are necessary to identify and address potential biases over time12. Prioritising equity and accuracy in educational materials, healthcare professionals can cultivate a deeper understanding of diverse populations and deliver more equitable care13.

Quality and accuracy:
Incorporating nurses and midwives in validation processes of AI-generated educational content is essential to uphold high standards of clinical accuracy and educational effectiveness. Although some aspects of clinical validation might be aided by AI, nuanced judgment and contextual decision making provided by nurses and midwives are crucial. Their firsthand clinical experience and expertise enables them to assess the practical applicability of educational materials, ensuring these resources are accurate, pragmatic, and directly applicable in varied healthcare settings. This human oversight is critical for incorporating professional standards and evidence-informed guidelines that AI alone might misinterpret or apply too rigidly14.

Nurses and midwives contribute personal insights and tacit knowledge derived from direct patient interactions, offering a depth of understanding that algorithms alone cannot replicate. Their involvement is crucial in tailoring educational content to address specific learning objectives and ensuring it is culturally competent and relevant to actual clinical scenarios. Evaluating the subtleties of learner engagement and practicality of teaching methods, which are influenced by cultural, ethical, and interpersonal factors, they add invaluable perspectives enhancing the quality of education14.
Active engagement of nursing and midwifery experts in validation processes allows educational materials to effectively communicate complex clinical concepts and procedures. This helps cater to diverse learning styles and promotes optimal knowledge retention and application. Despite the capabilities of AI, the irreplaceable input from nurses and midwives ensures the educational content meets clinical standards and resonates with realities of professional practice, thus bridging the gap between theoretical knowledge and practical application.

Preparing the workforce for a digital future
Nursing and midwifery workforces have already begun to integrate new technologies into healthcare delivery, a trend accelerated by the COVID-19 pandemic and the rapid deployment of telehealth and other digital solutions. Whilst this integration continues, the future workforce needs to be aware of and address the ongoing challenges accompanying technological advancements.

Skills development:
Incorporating digital literacy and AI competencies into curricula for nursing and midwifery is crucial to preparing future healthcare professionals for an increasingly technology-driven environment. Research shows significant potential for AI in enhancing educational outcomes in nursing15. Incorporating digital literacy and specific AI competencies into curricula for nursing and midwifery is crucial. Digital literacy covers the broad ability to evaluate, use, and adapt various digital technologies, while AI literacy focuses on understanding the principles and applications of AI technologies, as well as their ethical implications in healthcare. Both forms of literacy are essential for nurses and midwives to leverage technology effectively in improving patient care and enhancing clinical decision making. By integrating these skills and knowledge into educational curricula, nurses and midwives can more effectively leverage technology to improve patient care, enhance clinical decision making, and drive healthcare innovation.

Studies show healthcare professionals with digital literacy and AI capabilities and competencies are better equipped to navigate electronic medical records, utilise clinical decision support systems, and engage with telehealth platforms, ultimately leading to more efficient and effective healthcare delivery16. While it is advantageous for all nurses and midwives to have foundational understanding of digital and AI technologies, depth of expertise might vary based on specific roles and responsibilities. For example, a nurse working in a technologically advanced urban hospital might use AI-driven diagnostic tools and data analytics extensively for predictive modelling and personalised care planning. In contrast, a community health nurse might primarily use VR/AR assisted telehealth platforms to reach patients in remote areas. Both scenarios underscore the importance of tailored training that equips each professional with the necessary skills to optimise their impact within their particular context.

Ethical use of AI:
Educating nursing and midwifery students about ethical use of AI in healthcare, including GAI, is essential to ensure future healthcare professionals uphold principles of patient autonomy, privacy, and the preservation of the human touch in patient care. Understanding ethical implications of AI technologies, including the unique challenges posed by generative models that can create or simulate patient data and scenarios, enables students to navigate complex ethical dilemmas and make informed decisions that prioritise well-being and safety17.

Issues related to consent and privacy are particularly pertinent in the context of AI-driven healthcare interventions, such as data-driven clinical decision support systems and predictive analytics. Additional complexities are introduced as it can generate realistic but synthetic patient data and scenarios, which may be indistinguishable from real data. Students must learn to uphold patients’ rights to informed consent and confidentiality, particularly when utilising GAI technologies that involve the collection, storage, and synthesis of sensitive health data. Emphasising the importance of maintaining the human touch in patient care helps students recognise the limitations of AI and the irreplaceable value of empathy, compassion, and interpersonal communication in fostering therapeutic relationships and providing holistic care18.

Integrating ethics education into nursing and midwifery curricula, educators can empower students to critically evaluate the ethical implications of traditional and generative AI technologies and advocate for ethically responsible practices in their future clinical roles. This prepares them to navigate the ethical complexities of AI-driven healthcare environments while upholding the highest standards of professional integrity and patient-centred care.

Conclusion
Generative AI is setting the stage for a revolutionary shift in nursing and midwifery education, enhancing it to be more adaptive, such as through multisensory simulations, and personalised learning experiences. This evolution is critical as it empowers future nurses and midwives to deliver superior care, an essential development in our rapidly evolving healthcare landscape. While GAI reshapes educational methodologies across various healthcare disciplines, its integration into nursing and midwifery is notable due to the unique demands of these fields. These areas require a profound understanding of person-centred care, emotional intelligence, and interdisciplinary collaboration, which GAI can uniquely enhance by providing rich, scenario-based learning experiences explicitly tailored to these needs.

However, advancing this technology requires a rigorous focus on ethics, privacy, and preserving the irreplaceable human elements of healthcare. Educators, policymakers, and leaders must actively participate in integrating GAI technologies thoughtfully into their curricula. This involves embracing technological innovations and addressing the accompanying challenges and ethical dilemmas.

As nursing and midwifery continue to adapt to technological advancements, integration of generative AI presents unprecedented opportunities to amplify these efforts. By harnessing GAI, nursing and midwifery educators can significantly enrich learning environments and ensure their teachings remain cutting-edge. It is crucial that nursing and midwifery advance with a commitment to innovation, integrity, and insight, positioning GAI as a bridge to a future where technology not only supports but enhances the human aspects of healthcare.

Cedar Yin CHIA Dr Helen Almond FAIDH Dr Jen Bichel-Findlay FAIDH CHIA Dr Zerina Tomkins
May 2024

References

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