Artificial Intelligence (AI) is revolutionizing various industries, and healthcare is no exception. In nursing education—both in the academic and practice settings—AI tools are increasingly being integrated into teaching methodologies, offering unprecedented opportunities for enhancing learning experiences, improving clinical decision-making, and preparing learners for the complexities of modern healthcare (Shepard, 2023; Shepard & Griesheimer, 2024). However, the rapid adoption of AI also presents significant ethical, legal, and practical challenges. The literature underscores the potential for implicit and explicit biases in AI due to human-created datasets and models and AI’s lack of context and real-life experience (Obermeyer et al., 2019; Christensen et al., 2021; Agarwal et al., 2023). This potential for bias underscores the urgent need for thoughtfully crafted policies to govern the use of AI in nursing education, ensuring that these tools are used effectively and responsibly.

Policies should guide the implementation of AI in education, ensuring the technology complements rather than replaces traditional teaching methods. Policies should mandate that AI tools are used as adjuncts to human instruction, preserving the critical role of nurses who claim nursing professional development (NPD) as their specialty. Traditionally, these nurses fulfill nursing education roles and are integral in ensuring content integrity and fostering critical thinking, empathy, and ethical decision-making. Without specific AI guidelines, over-reliance on AI could diminish the quality of education due to reduced critical appraisal of AI outputs, thus allowing the potential for bias in educational content.

Nurse educators must create policies around the use of AI to mitigate risks to security, accuracy, and educational integrity when utilizing AI to develop educational activities, supplement content, and analyze evaluation data. Understanding and leveraging AI aligns with organizational goals, emphasizing emerging technologies to enhance patient outcomes. Responsible and ethical AI usage offers nurse educators an innovative way to streamline educational content creation, enhance adult learning principles, and increase efficiency without compromising content integrity.

Organizational goals related to diversity, equity, inclusion, and belonging (DEIB) should be considered with AI usage to reduce or eliminate bias and promote inclusivity. This approach is consistent with the Accreditation Council for Continuing Medical Education (ACCME) Standards for Integrity and Independence in Accredited Continuing Education (2021) and Nursing Professional Development Standards 7. Ethics, and 9. Respectful and Equitable Practice (Harper & Maloney, 2022). The NPD Practice Model emphasizes that quality inputs lead to quality outputs, with nurse educators significantly impacting inputs and throughputs related to AI implementation. Nurse educators must gain knowledge regarding AI’s appropriate use to achieve unbiased outputs, thereby safeguarding the ethical use of AI. The CO-STAR framework (Context, Objective, Scope, Tone, Audience, Result) can guide NPD practitioners in a structured approach to ensure relevance, clarity, and inclusivity in generated responses (Medium, 2024).

Policies should clearly define the roles and responsibilities of nurse educators when using AI tools within the educational design process. Nurse educators should retain control over key aspects of educational design, ensuring that AI is used to support, not replace, their professional expertise.

References

Accreditation Council for Continuing Medical Education. (2021). Standards for Integrity and Independence in Accredited Continuing Education. https://accme.org/wp-content/uploads/2024/05/884_20220623_Standards-for-Integrity-and-Independence-in-Accredited-Continuing-Education.pdf

Agarwal. R., Bjarnadottir, M., Rhue, L., Duggs, M., Crowley, K., Clark, J., & Gao, G. (2023). Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework. Health Policy and Technology, 12(1), https://doi.org/10.1016/j.hlpt.2022.100702

Christensen, D. M., Manley, J., & Resendez, J. (2021). Medical algorithms are failing communities of color. Health Affairs Forefront. https://doi.org/10.1377/forefront.20210903.976632

Harper, M. G. & Maloney, P. (2022). Nursing Professional Development Scope and Standards of Practice. 4th Ed. Association for Nursing Professional Development

Medium. (2024 January 19). Unlocking the power of COSTAR prompt engineering: A guide and example on converting goals into system of actionable items. https://medium.com/@frugalzentennial/unlocking-the-power-of-costar-prompt-engineering-a-guide-and-example-on-converting-goals-into-dc5751ce9875

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations.

Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342

Shepherd, J. (2023). Unlocking the future of nursing education and continuing professional development by embracing generative artificial intelligence and advanced language models. Interdisciplinary Journal of Partnership Studies, 10(2), https://doi.org/10.24926/ijps.v10i2.5614

Shepard, J. & Griesheimer, D. (2024 June 4). FAQs: AI and prompt engineering. American Nurse. https://www.myamericannurse.com/faqs-ai-and-prompt-engineering/