Explainable Artificial Intelligence for Personalized User Experience Optimization in Intelligent Interactive Systems

Abstract

Intelligent interactive systems increasingly adapt interfaces, recommendations, notifications, and task pathways to heterogeneous users. Yet conventional personalization frequently optimizes short term engagement without providing understandable reasons for adaptation, or safeguards against unstable, intrusive, and inequitable outcomes. This paper develops an explainable artificial intelligence (XAI) framework integrating contextual user representation, multiobjective decision making, local feature attribution, counterfactual explanations, and a user feedback loop. The architecture separates preference estimation from policy selection and explanation delivery, enabling auditability of both prediction and action. A rigorous evaluation protocol is specified for task completion, satisfaction, cognitive load, explanation fidelity, fairness, latency, and privacy. To demonstrate reporting procedures without implying that an experiment has been conducted, a fully labeled synthetic scenario compares static, opaque adaptive, and explainable adaptive interfaces. Illustrative calculations indicate the types of tradeoffs that a deployment study should test, not empirical evidence of superiority. The framework contributes a reproducible system blueprint, mathematical formulation, implementation workflow, and validation plan for transparent, user centered adaptive interaction.