Letter to the Editor by Fatemeh Choupani to OJIN topic: “Artificial Intelligence in Nursing and Healthcare”

March 16, 2026

Response by Fatemeh Choupani to OJIN topic: “Artificial Intelligence in Nursing and Healthcare” (May 2025)

Dear Editor,

I commend OJIN’s recent coverage of artificial intelligence (AI) and design thinking (DT) in nursing education, particularly Knighton’s (2025) overview of DT’s role in fostering innovative pedagogy and Wieben’s (2025) exploration of AI’s transformative potential. These articles highlight DT’s empathy-driven approach and AI’s benefits, such as enhanced clinical simulations. However, a critical ethical gap persists: the need for systematic bias auditing within AI-driven DT frameworks to ensure equitable nursing education.

Knighton (2025) emphasizes DT’s value in curriculum design, while Talsma et al. (2025) demonstrates its impact on graduate student engagement. Neither integrates AI nor addresses algorithmic bias risks. Similarly, Wieben (2025) and Dillard-Wright and Smith (2025) advocate ethical AI in practice but underexplore bias auditing in educational contexts. My work proposes embedding bias audits into DT’s Define and Test stages to address these gaps, ensuring AI tools like predictive analytics and virtual simulations promote equity.

In the Define stage, I propose auditing datasets for representativeness to avoid skewing problem identification (e.g., underrepresenting minority groups) (Bellamy et al., 2019). In the Test stage, equity audits should evaluate AI tools for differential performance across demographics, as seen in AI-driven educational systems that reduced bias through iterative testing (Zawacki-Richter et al., 2019). Faculty training in AI literacy is essential to navigate AI’s “black box” opacity, ensuring human oversight complements technology (Dillard-Wright & Smith, 2025).

This equity-driven approach transforms DT into a framework for technical ethics, preparing students to critique AI health technologies in practice. For instance, auditing simulations for diverse patient representations aligns with holistic care principles. Unlike Wieben (2025), which focuses on clinical AI, my framework targets educational equity, extending Talsma et al.’s (2025) DT module by integrating audited AI tools.

I urge nurse educators to foster AI literacy focused on bias auditing, aligning with OJIN’s call for ethical innovation. I encourage OJIN to consider a dedicated topic on bias auditing in AI-driven nursing education to advance this discourse. By embedding audits into DT, educators can prepare students for technology-rich, equitable healthcare. I welcome feedback from the OJIN community on operationalizing ethical AI-DT integration.

Sincerely,
Fatemeh Choupani, PhD, RN
Assistant Professor, Seattle University College of Nursing and Health Science