Integrating Artificial Intelligence Education and Design Fiction Pedagogy in Nursing
According to Li and Bertrand (2025), the Design Fiction Pedagogy (DFP) model represents an innovative approach to teaching AI that integrates speculative design and narrative learning. Grounded in constructivist and constructionist learning theories, DFP encourages learners to imagine, prototype, and evaluate AI-based futures. The model includes seven pedagogical steps: identifying a problem, designing a prototype, creating a future context, building a narrative, sharing with stakeholders, reflecting on ethics, and redesigning. Through this process, learners engage in iterative and collaborative problem-solving while considering the societal and ethical consequences of emerging technologies.
Li and Bertrand’s (2025) research demonstrates that by encouraging students to build both physical and digital prototypes, such as AI devices for healthcare or environmental applications, learners develop critical and creative thinking. Importantly, DFP helps students understand that technological innovation must align with ethical and social responsibility.
Application in Post-Secondary Nursing Education
In a post-secondary nursing context, both frameworks support the development of technologically fluent, ethically grounded, and globally competent practitioners. Nursing education is increasingly intertwined with digital technologies such as electronic health records (EHRs), predictive analytics, and telehealth platforms. Embedding AI literacy into nursing curricula aligns with the AI for K-12 “Big Ideas,” particularly the concepts of machine learning and ethical impact.
For example, nursing students can analyze how AI supports diagnostic decision-making while critically examining potential algorithmic biases that could disadvantage certain populations. Integrating DFP methods into simulation-based courses could enhance this learning. Students might design speculative AI tools, such as decision-support apps for triaging patients or monitoring chronic illness, and then reflect on ethical implications like privacy, autonomy, and access. This mirrors Li and Bertrand’s (2025) approach, which encourages learners to design, narrate, and refine AI innovations while considering their real-world effects.
These pedagogical strategies would also strengthen critical digital literacy by empowering nursing students to become discerning evaluators of health technologies rather than passive users. By combining technical understanding with ethical reflection, educators can cultivate nurses who view AI not as a threat to clinical judgment but as a collaborative partner that requires constant human oversight and moral reasoning.
Application in Hospital and Healthcare Settings
In professional healthcare environments, these frameworks can transform how continuing education and clinical training are delivered. Hospitals increasingly rely on AI-driven systems for patient monitoring, resource allocation, and predictive analytics. Yet many frontline nurses and allied health professionals are not trained to understand how these technologies function or how bias can affect patient outcomes.
Applying Touretzky et al.’s (2019) framework in hospital education programs can address this gap. Workshops could introduce staff to the fundamentals of how AI systems collect and interpret data, how algorithms support (and sometimes distort) decision-making, and how ethical principles like transparency and fairness apply to clinical technology. Nurses can learn to question data sources, understand model limitations, and recognize when human intervention is essential to patient safety.
Integrating Li and Bertrand’s (2025) DFP approach into hospital learning initiatives offers a complementary, hands-on method. For instance, interdisciplinary teams could use speculative design to envision future healthcare scenarios, such as AI-assisted patient triage or robotic medication delivery. Participants would build and critique these “future prototypes,” reflecting on issues of accountability, equity, and empathy. This narrative-driven reflection fosters creative problem-solving and anticipatory thinking. These skills are vital in rapidly evolving health systems.
By applying DFP, hospital educators can transform abstract ethical discussions into tangible learning experiences. This method supports professional resilience and adaptability, preparing clinicians to navigate an environment where automation and human care increasingly intersect.
Implications for Educational Leadership
Together, the AI for K-12 framework and the Design Fiction Pedagogy model advocate for a shift in both nursing education and healthcare professional development, from training users of technology, to cultivating critical co-designers of digital health systems. Educators and clinical leaders must ensure that learners develop not only technical competence but also reflective, ethical, and imaginative capacities.
In post-secondary settings, this means embedding AI literacy within existing nursing competencies, using active learning, simulation, and cross-disciplinary collaboration. In hospitals, it involves redesigning staff education to include applied ethics, participatory design, and interdisciplinary dialogue about AI integration. These approaches align with contemporary nursing standards that emphasize lifelong learning, patient advocacy, and evidence-informed decision-making.
Ultimately, fostering AI literacy through critical and creative pedagogies empowers nurses to shape the technological future of healthcare responsibly. This balancing must include innovation with humanity, and efficiency with empathy.
A nurse educator who actively practices in a clinical environment holds a distinctive vantage point for bridging the theoretical frameworks of AI literacy with the pragmatic realities of healthcare delivery. My own experience as a clinical leader, overseeing respiratory therapists and nurses in technologically intensive settings, has revealed both the promise and pitfalls of AI-driven systems. Tools such as predictive monitoring platforms, digital documentation systems, and algorithm-based triage models illustrate the Big Ideas outlined by Touretzky et al. (2019): computers perceive, model, and learn from data. Yet these same tools also surface the fifth principle, the dual social impact of AI, when biases or technical failures directly influence patient outcomes.
In the post-secondary classroom, this lived experience becomes a powerful pedagogical asset. Rather than teaching AI as an abstract concept, presenting students with authentic clinical scenarios, such as when an algorithm misclassified a patient’s risk score or when automated documentation conflicted with human assessment. By framing these incidents through Li and Bertrand’s (2025) Design Fiction Pedagogy (DFP), students can reimagine the situation, prototype alternative system designs, and reflect on the ethical and practical implications of their choices. This process transforms AI literacy into critical digital citizenship, where learners connect theoretical ethics to patient safety, advocacy, and accountability.
In the hospital environment, however, this dual role introduces complexity. Staff education often prioritizes procedural compliance and efficiency, leaving limited space for speculative or ethical inquiry. Implementing DFP-inspired learning here requires cultural change, creating psychologically safe environments where clinicians can critique technology without fear of reprisal. It also demands interdisciplinary collaboration, ensuring nurses’ experiential insights shape how AI tools are implemented and audited.
Li, L., & Bertrand, M. (2025). 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
Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI? Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence.
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