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

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

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Please note this is a comparison between Version 2 by Abigail Zou and Version 1 by Samad Sepasgozar.

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.

AI Calibracy
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An analysis of 9247 AI-related job advertisements found limited explicit representation of competency requirements for professionals expected to use AI operationally in their everyday work ^[1]. The observations reveal that the AI-related job advertisements were concentrated simply on roles involved in developing, implementing, and managing AI systems. The study nevertheless highlighted the need for operational users to possess sufficient technical and domain understanding to interpret and explain algorithmic outputs ^[1]. Technical understanding may enable graduates to operate AI systems and comprehend their outputs, but it does not necessarily explain how users should determine the appropriate authority of an AI recommendation when it interacts with disciplinary judgment, contextual evidence, and analytical models. This paper introduces a theoretical concept, named AI Calibracy, that explicitly addresses this additional educational problem. This established conceptual framework examines how graduates use and adjust their trust in different sources, such as themselves, evidence, computer models or simulations, and AI-generated insights, when making decisions.

Questions about graduates’ futures are becoming more important as AI advances rapidly. This necessitates specific skills and competencies that scholars describe and identify differently. For example, Lin and Sohail ^[2] suggest them as meta-skills, referring to skills related to determining task suitability for AI, discerning outputs, and calibrating results. AI’s advances and its ability to solve complex problems raise many questions, such as:

What should graduates be able to do, or what should students learn to remain capable and relevant in rapidly changing professions? What competencies will they need for current and future workplaces? How can individuals strengthen their capabilities, and how should universities redesign their curricula to develop the skills that future graduates will need? What should you do when AI recommends one option, the available evidence points to another, an analytical model relies on uncertain assumptions, and your professional experience suggests a different approach? Which source should shape the decision? Should they all receive equal weight? How much influence should AI be given? And when conditions change, can we explain why we changed both our decision and our reliance on each source?

These questions are becoming increasingly important as AI moves from producing text and summarizing information to participating in analysis, design, forecasting, and professional decision-making. These questions are increasingly being discussed in the education context, and they motivate clarifying the relevant concepts for AI use.

1.1. Professional Problem: Multiple Contributors to Judgment

Professionals may not decide between humans versus AI. Instead, they may simultaneously rely on their own disciplinary judgment and empirical evidence where available and use AI output. These may disagree, or they may be less experienced and so rely heavily on AI outputs. For example, an engineer may have an initial interpretation based on disciplinary knowledge, sensor evidence suggesting another interpretation, and an AI system producing yet another recommendation. A clinician [3^[3][4],4], physician ^[5], planner, manager ^[6], researcher, or construction professional may encounter an equivalent configuration in various contexts and need to calibrate AI outputs from different perspectives ^[7]. Lin and Sohail ^[2] suggest that specific capabilities such as determining task suitability, discerning outputs, and calibrating results in the context of academic skills and the necessity of meta-skills, when people use generative AI (GenAI). The professional or graduate challenge is therefore not simply whether AI is accurate, but whether the individual possesses the capabilities necessary to examine these different contributors, understand their limitations and contextual relevance, and develop a defensible judgment.

1.2. Overview of the Conceptual Development Approach

This entry adopts a theory-synthesis and conceptual-model-development approach rather than an empirical design. This approach has been used in previous studies ^[8]. Its purpose is not to provide a systematic review or an exhaustive mapping of the literature, but to develop and delimit the concept of AI Calibracy by integrating a theoretical approach that addresses different aspects of reliance regulation in AI-supported judgment.

The literature was selected purposively around the focal conceptual problem of how a user evaluates and revises reliance when professional judgment is informed by personal judgment, empirical evidence, analytical models or simulations, and AI-generated outputs. The literature also refers to behavioural aspect of appropriate reliance, including relative AI reliance (RAIR) [9,10]^[9][10] and relative self-reliance ^[11] associated with the appropriateness of reliance (AoR) ^[9].

At present, the literature has a large focus on AI literacy and competency and also refers to various concepts to be learned and considered, such as trust and reliance, calibration, appropriate reliance on AI outcomes, metacognition and self-regulated learning, information and evidence evaluation, epistemic vigilance, model validation, professional judgment, and human–AI decision-making.

This entry considers Self, Evidence, Model/Simulation, and AI (SEMA), since each can provide analytically distinct bases of warrant that may fail for different reasons. Their integration is important for a more reliable outcome; therefore, AI Calibracy considers linking source-specific appraisal with cross-source comparison, metacognitive regulation, feedback, and development across repeated decision episodes. The link and integration of sources to be applicable to decisions as the outcomes of AI-powered systems give AI Calibracy its distinctiveness. To distinguish established knowledge from the organizing synthesis used in this Entry, three evidential levels are maintained throughout the paper. First, established constructs and measures are drawn from the literature, including AI literacy and competency, trust and reliance, calibration, appropriate reliance, metacognition, self-regulated learning, and existing reliance measures. Second, AI Calibracy, SEMA, and the reliance calibration and regulation cycle (RCRC) are used here as integrative conceptual devices for organizing relationships among those established constructs. Third, the graduate archetypes, construction vignette, and candidate future process measures.

References

Anton, E.; Behne, A.; Teuteberg, F. The Humans Behind Artificial Intelligence—An Operationalisation of AI Competencies; AIS Electronic Library: Atlanta, GA, USA, 2020.
Lin, Z.; Sohail, A. Recalibrating academic expertise in the age of generative AI. Patterns 2026, 7, 101473.
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Cabitza, F. Calibration-informed metrics for instance-level predictive reliability in medical AI. Artif. Intell. Med. 2026, 174, 103366.
Patil, S.V.; Myers, C.G.; Lu-Myers, Y. Calibrating AI reliance—A physician’s superhuman dilemma. In JAMA Health Forum; American Medical Association: Chicago, IL, USA, 2025.
Haque, E.A. The Role of Calibration Engineering In Strengthening Reliability Of US Advanced Manufacturing Systems Through Artificial Intelligence. Rev. Appl. Sci. Technol. 2025, 4, 820–851.
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Pound, P.; Campbell, R. Exploring the feasibility of theory synthesis: A worked example in the field of health related risk-taking. Soc. Sci. Med. 2015, 124, 57–65.
Schemmer, M.; Kuehl, N.; Benz, C.; Bartos, A.; Satzger, G. Appropriate reliance on AI advice: Conceptualization and the effect of explanations. In Proceedings of the 28th International Conference on Intelligent User Interfaces; Association for Computing Machinery: New York, NY, USA, 2023.
Küper, A.; Krämer, N. Psychological traits and appropriate reliance: Factors shaping trust in AI. Int. J. Hum.-Comput. Interact. 2025, 41, 4115–4131.
Raees, M.; Papangelis, K. Trust to Reliance: Measurement Constructs for Human-AI Appropriate Reliance. In Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems; Association for Computing Machinery: New York, NY, USA, 2026.