Generative AI and Design Fiction in Nursing Education: A Reflection

Generative Artificial Intelligence (GenAI) can transform teaching and learning in both academic and clinical nursing environments. Bouguettaya et al. (2025) describe GenAI as marking a paradigm shift from traditional digital learning toward personalized, adaptive, and generative experiences that respond to individual learner needs. In nursing education, this resonates with our movement toward competency-based learning and simulation-based assessment, where every learner’s path is unique but still anchored to clinical standards of practice.

As an educator, I was particularly struck by how Li and Bertrand (2026) framed the Design Fiction Pedagogy (DFP) as a method for fostering critical and ethical thinking about technology. DFP encourages learners to imagine the future implications of AI through narrative and prototyping. Translating this into nursing, I could envision a learning activity where students design a “future clinical scenario” involving an AI diagnostic assistant. This would not only teach clinical reasoning, but also provoke ethical dialogue about accountability and human oversight in patient care. The hands-on, reflective, and repetitive nature of DFP parallels the way we teach clinical judgment using simulation and debriefing.

Connections to Practice

In the postsecondary nursing classroom, I can see DFP’s application in ethics or professional practice courses. Students could create speculative narratives exploring what “autonomy” means when AI makes patient-care recommendations. For instance, what happens when a decision-support system contradicts a nurse’s intuition? These activities would cultivate critical reflection and ethical awareness, and skills Bouguettaya et al. (2025) identify as essential for GenAI literacy.

In the clinical environment, AI already shapes documentation, predictive analytics, and staffing systems. Integrating reflection on these tools could make the hidden algorithms visible. Encouraging staff or students to “prototype” what a transparent AI decision tool might look like.  This would mirror DFP’s creative and critical process while addressing practical issues like algorithmic bias and clinical accountability.

Critical Integration of Theory

Li and Bertrand’s (2026) research grounded DFP in constructivist and constructionist learning theories. These frameworks emphasize that knowledge is co-created through experience and reflection. This principle deeply aligns with nursing preceptorship. When I think of bedside teaching, each patient encounter becomes a speculative narrative where nurses and learners co-create understanding. The “artifact” is not a physical prototype, but the clinical decision itself, shaped by ethical reflection, communication, and repetitive reasoning.

Bouguettaya et al. (2025) extend this theoretical grounding into the realm of AI-enhanced personalized learning, noting that generative systems can serve as cognitive partners. In nursing education, this means AI could scaffold a learner’s clinical reasoning by providing structured feedback.  This would act similarly to an intelligent tutoring system. However, the risk lies in what they call “over-reliance” on AI. This echoes my own concerns about automation bias. The tendency for healthcare professionals to trust the machines output even when it contradicts clinical evidence. This can have dangerous outcomes.

Reflections on Learning Process

From a declarative knowledge perspective, I learned how GenAI tools can support knowledge construction through adaptive tutoring, content generation, and scenario-based learning. Procedurally, I learned that fostering AI literacy in nursing education requires deliberate design thinking by creating learning experiences that push students to critically engage with technology rather than passively use it.

This also reinforced that integrating AI responsibly in education and healthcare demands interdisciplinary collaboration. As Bouguettaya et al. (2025) emphasize, educators must balance innovation with ethics, ensuring that human judgment remains central. Reflecting on my own experience implementing new simulation software, I realize that effective technology adoption hinges not just on training but on dialogue.  This helps educators and learners co-create meaning around the tool’s purpose.

Application Ideas

Postsecondary Nursing Course: Implement a DFP-inspired capstone where students design “AI-in-practice” ethical scenarios, combining speculative storytelling and real-world data analysis.

Clinical Practice: Use AI-driven reflection tools (e.g., generative journaling prompts) during debriefs to help nurses analyze emotional and ethical dimensions of patient care.

Research: Explore how AI literacy correlates with critical thinking scores in nursing students, and examining whether design-based pedagogies improve reflective judgment.

Conclusion

This reinforces that the intersection of Generative AI and nursing education is not just a technological evolution but a profound pedagogical shift. The attached articles encourage thinking beyond the novelty of AI tools. Instead the focus is on their capacity to enhance critical thinking, ethical reflection, and human learning. I realized that integrating AI effectively is less about mastering software and more about shaping a mindset.  The mindset needs to be one that values inquiry, creativity, and compassion.

In both postsecondary and clinical contexts, this means fostering a culture where educators and learners co-construct knowledge with AI, critically interrogating its role, rather than accepting it at face value. Whether designing speculative scenarios in a classroom or engaging with AI-based clinical decision support tools, the ultimate goal remains the same.  It is meant to prepare nurses who are not only technologically competent but also ethically grounded and reflective practitioners.

The process of reflecting on these ideas reminded me that technology should never overshadow humanity. Rather, it should amplify it. 

Personal Reflection 

Therefore, how can we integrate Generative AI tools into nursing education in a way that strengthens, not replaces, human empathy and ethical reasoning?

I believe the answer lies in co-designing AI experiences that require emotional engagement and moral reflection. Just as Li and Bertrand (2026) emphasize the importance of narrative and storytelling in Design Fiction Pedagogy, nursing educators can frame AI as a collaborator rather than a replacement. For instance, asking students to debate or “interview” an AI assistant about an ethical dilemma can foster metacognition.  This can help them recognize both the utility and limitations of machine reasoning. In doing so, we ensure that technology remains a means of deepening human connection, not diminishing it.

References:

Bouguettaya, S., Pupo, F., Chen, M., & Fortino, G. (2025). A meta-survey of generative AI in education: Trends, challenges, and research directions. Big Data and Cognitive Computing, 9(9), 237. https://doi.org/10.3390/bdcc9090237

Li, L., & Bertrand, M. (2026). Fostering critical thinkers and future designers: Design fiction pedagogy in AI education. Thinking Skills and Creativity, 59, 101962. https://doi.org/10.1016/j.tsc.2025.101962