From Users to Co-Designers: A Data-Informed Pedagogical Framework for Fostering Critical AI Literacy in Professional Education

Cultivating critical AI literacy among future professionals is a pressing challenge in digital education. This study presents and evaluates a data-informed, four-phase pedagogical framework—Deconstruct, Empathize, Co-Design, Reflect—implemented within a Nursing Informatics course as a case study. Using an explanatory sequential mixed-methods design, we collected pre- and post-intervention survey data from 40 students and conducted follow-up focus group interviews. Findings indicate that the intervention group showed significant gains over the control group in critical thinking disposition (*t* = 3.41, *p* = .001, *d* = 1.05), technology self-efficacy (*t* = 2.91, *p* = .005, *d* = 0.92), and co-design engagement (*t* = 4.13, *p* < .001, *d* = 1.12), with moderate-to-strong correlations among these variables (*r* = .522–.739). Thematic analysis revealed a shift in students’ professional identity—from passive users to active co-designers—and demonstrated how domain-specific knowledge served as “design capital” in technology shaping. This research offers an evidence-based, transferable framework for digital education, demonstrating how data-driven empathy and participatory design can be integrated to equip students across disciplines with the competency to critically evaluate and co-create the AI technologies that will shape their future professions.