AI Calibracy: A Future-Ready Competency Framework in AI-Supported Professional Judgment and Decision Making

Abstract

AI Calibracy is a developmental competency that is defined in this entry as a learner’s or practitioner’s capacity to examine, coordinate, and progressively improve their judgment, external fresh evidence, computational models or simulations, and artificial intelligence (AI) within a specific decision context. What can be progressively improved is categorized into four bases of reliance, named as SEMA: Self, Evidence, Model or Simulation, and AI, and discussed in the paper. AI Calibracy is not supposed to prescribe a fixed hierarchy, numerical weighting, or a universal division of authority among these contributors. Instead, it identifies the capabilities required to recognize their relevance, limitations, assumptions, disagreements, and implications for professional judgment. The competency is introduced and also developed through the reliance calibration and regulation cycle (RCRC), as a process in which learners form an initial judgment, inspect evidence, test models, interrogate AI outputs, compare findings, identify uncertainty, revise their judgment and confidence, and reflect on outcomes across repeated tasks. In this concept, AI includes not only generative AI but also various functions such as predictive, diagnostic, recommendation, optimization, computer-vision, decision-support, and autonomous or semi-autonomous systems. AI Calibracy provides a definition for leadership, educators, curriculum design, pedagogy, professional development, assessment, and research into the capabilities required for responsible, as task-specific warrant and context-sensitive practice in AI-enabled environments.