From Prediction to Pedagogical Action: a Conceptual Framework for AI-Enabled Personalized Education

Abstract

Artificial intelligence can estimate learner states, diagnose difficulties, and predict performance, but these outputs do not themselves constitute personalized education. This conceptual article examines the transition from prediction to pedagogical action by distinguishing three functions that are often conflated: AI-enabled inference or prediction, pedagogical decision, and learner-facing action. It proposes a recurrent framework in which learner data support inference; predictions inform pedagogical decisions through objectives, available actions, decision rules or policies, and contextual constraints; decisions are realized as learner-facing actions; and learner responses and outcomes generate new evidence for subsequent adaptation. Five overlapping dimensions of personalized action are considered: content, sequence, assessment, pacing, and support or feedback. The framework emphasizes that predictive accuracy does not establish pedagogical appropriateness or educational effectiveness. Its contribution is to clarify how AI-generated predictions can be translated into pedagogically grounded actions within personalized education.