Integrating Large Language Models and Artificial Intelligence in Nursing Education

Submitted by Glenn Ford D. Valdez PhD, RN, CNE -cl

Tags: artificial intelligence future of nursing medical technology Nurse Education technology

Integrating Large Language Models and Artificial Intelligence in Nursing Education

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The advancement of technology and changing health demands in recent years, combined with the looming possibility of outbreaks and health crises, has shifted the world's attention to the digital space and the rise of assistive platforms, which contributes to continuous progress in the aftermath of lockdowns and quarantine mandates during the COVID-19 pandemic. Despite the hurdles, educational institutions discovered that moving into the digital realm and using numerous platforms helped to improve overall program implementation. Nursing education has found it difficult to initially integrate into this process because, like other health-related programs, caring for the sick cannot be replaced by a computer monitor; a basic principle of practice is to work with actual patients during clinical placements. Large language models and artificial intelligence proved to be quite useful in a variety of academic activities, including pre-testing and testing qualifying tests, as well as local board examinations (Menz et al., 2024; Roos et al., 2024; and Taira et al., 2023). Furthermore, AI and LLMs have made important contributions to improving patient outcomes in practice and enhancing patient care (Higgins et al. 2022; Yahagi et al. 2024).

LLMs and AI were also viewed as effective in enhancing healthcare administration through better data systems, clearer patient care, and improved communication (Makhlouf et al. 2024). In nursing education, the use of chatbots and virtual patients powered by artificial intelligence has proven effective in remotely instructing students (Shorey et al 2019). Generative AI was also utilized in nursing education for self-reflection and teaching undergraduate nurses in caring and developing nurse-patient connections (Reed et al,2033). Given the promising uses and achievements, difficulties such as disconnect, data privacy, and confidentiality were identified in various studies (Tezpal et al., 2024; Higgins et al., 2022; and Menz et al., 2024). The lack of connectivity is frequently considered as a weakness, as are innovations that make their way into academics and can either become extremely beneficial or be avoided due to their complexity. While robots and other machine automated devices were successful enough to make their way into nursing practice it's a different story when it comes to nursing academia. While robots and other machine-automated technologies were successful enough to make their way into nursing practice, the scenario is different in nursing academics. The implementation of large language models and artificial intelligence in nursing academia has yet to be achieved. It is thought vital that the preparedness and acceptance of these technologies be assessed and given professionally (Cho et al,2024). A positive attitude towards its use is a crucial aspect to consider (Lukić et al., 2024). Although LLMs and AI have major applications in nursing education practice and research, there are certain risks associated with their accuracy, bias, exploitation, and plagiarism (Hosenback et al,2024). Recognize AI's biases and restrictions in nursing practice, and proceed with caution while implementing it (Ye,2024). 

Nurse academics must be proactive in recognizing their critical position in an era where LLMs and AI will be deemed part of their tasks, whether used in didactics or clinical placements and practice. Integrating these technologies will prepare nursing students to better comprehend and enter clinical practice with competence and awareness. The following outlines the inherent responsibilities in the integration of LLMs and AI in the nursing academia. 

  1. To prepare students, teachers, and staff, awareness and professional training programs should be implemented to educate them on the benefits, applications, guidelines, and regulations of employing LLMs and AI.
  2. Evaluate the effectiveness of LLMs and AI in nursing courses through moderation and monitoring.
  3. Led assessment, standards, and evaluation to ensure accurate and effective use of LLMs and AI in nursing education.
  4. Ensure accuracy and confidence while employing LLMs and AI technology.
  5. LLMs and AI should not disrupt communication or substitute critical thinking and decision-making processes.
  6. Assist in identifying disinformation and conducting fact-checking to guarantee accurate and relevant information.
  7. Ensure ethical usage of LLMs and AI under higher education regulations and standards.
  8. Assess user satisfaction to monitor its effectiveness and relevance to student achievement.
  9. Educate nursing students on academic dishonesty, plagiarism, prejudice, and abuse policies, as well as the consequences of breaking them.
  10. Provide ongoing training programs for students and faculty on current applications of LLMs and AI in education.

To summarize, given their novelty, LLMs, and AI in nursing education require a more complete review of their direct implementation, as well as the issues that arise from them. Furthermore, ongoing efforts must be made to ensure its seamless integration into nursing education, whether via classroom didactics, nursing praxis, or clinical placements. To fully exploit the potential of these two technologies, there is a significant drive for more explicit and stringent policies about their acceptability, as well as the implementation of rules governing their use. Nurse educators today and tomorrow must navigate a future open to advancements and breakthroughs while upholding nursing's core ideals as an art and science. 

References    

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