Developing judgement for business: an AI-based model of independent management learning

• Introduces a novel, ranking-based explainable AI for developing expert judgment. • Designs interactive AI simulations for personalized, experiential learning and decision-making. • Identifies zones of uncertainty to target expert feedback efficiently in learning platforms. • Enables immediate, dialogue-driven feedback for self-reflection and skill development. The rapid acceleration in the sophistication, visibility and accessibility of machine learning and artificial intelligence (AI) technologies presents opportunities and challenges for management learning and education. We focus on two key elements: (i) weaknesses in current AI approaches in management learning and education; and (ii) the challenges of providing timely, personalized and reliable feedback that is particularly central to experiential learning designs. We propose a novel, ranking-based explainable AI approach that uses Adaptive Comparative Judgment (ACJ) theory and Gaussian statistical distributions that can be tailored to deliver structured self-directed and formal learning. We show how combining generative AI, our comparative judgment AI and dynamic simulations creates learning designs based on frequent, tailored, reliable and dialogue driven individualized feedback on management decisions, that builds deep skills in self-reflection and judgment.