Critical Fairness Literacy for PK–12 teachers: a fairness-specific dimension of teacher AI competency

As artificial intelligence (AI) becomes integrated into PK–12 education, teachers need professional judgment to evaluate its fairness. This conceptual paper develops Critical Fairness Literacy (CFL) through a structured synthesis of literature on teacher AI competency, critical AI literacy, algorithmic fairness, educational AI ethics, and educational equity. CFL is proposed as a subdimension of teacher AI competency focused on fairness, comprising bias source identification, contextual equity analysis, and interrogation of fairness definitions. Three practical protocols, bias tracing, equity-centered contextual review, and fairness framing reflection, connect these dimensions to classroom judgment and professional learning. The paper proposes how guided inquiry, peer feedback, and repeated application might support CFL development. The framework and protocols remain theoretically informed proposals requiring empirical investigation.