Assessing Cognitive Complexity in Student Interactions With AI-Based Learning Activities: Using Bloom’s Taxonomy to Foster Critical AI Literacy

BACKGROUND: Critics of AI note its potential to foster passivity, intellectual dependency, and reproduction of inaccurate or plagiarized content. Strategies are needed to support nursing students to engage critically with AI. PROBLEM: To develop teaching approaches that facilitate critical AI literacy, faculty need to understand what happens cognitively when students interact with various AI-based activities. APPROACH: In a curriculum design elective course for future nurse educators, 2 doctoral students explored cognitive outcomes of AI-based class assignments, which included comparing AI summaries of published articles; contrasting student and AI writing styles; reflecting on strategies of reasoning when using AI; and applying Bloom's taxonomy to code the cognitive complexity of reflective essays. CONCLUSIONS: A 4-stage instructional approach supported high-level reflection regarding AI impacts on thinking and writing and enabled students to learn about objectives-driven lesson planning. This model can inform AI-literacy lesson development and help prepare future educators for AI-permeated teaching environments.