Asian Basic and Applied Research Journal

Artificial intelligence (AI) systems increasingly support decisions in environments where data distributions, operational conditions, user needs, and risk levels change over time. Models that perform well during development may become unreliable after deployment because of data drift, concept drift, sensor degradation, population changes, or unexpected interactions. At the same time, predictive performance alone is insufficient for high-impact decisions: users need explanations that are understandable, relevant to the decision context, and accompanied by appropriate uncertainty information. This paper proposes an Adaptive Explainable AI (A-XAI) framework intended to integrate reliability monitoring, explanation generation, uncertainty estimation, human oversight, and controlled model adaptation within a unified lifecycle. The framework contains nine conceptual components: contextual data acquisition; data-quality, privacy, and drift assessment; prediction with uncertainty estimation; context-sensitive explanation generation; decision support and human oversight; risk-aware reliability gating; feedback and audit logging; controlled adaptation; and post-adaptation revalidation with version control. A formal decision policy is presented to connect predictive confidence, drift evidence, explanation quality, and consequence severity to actions such as automated recommendation, human review, abstention, or retraining. The paper also proposes an evaluation protocol spanning predictive performance, calibration, robustness, explanation fidelity and stability, fairness, latency, and operational outcomes. As a conceptual contribution, the framework is not presented as experimentally validated; its effectiveness must be tested in domain-specific studies using real deployment data and preregistered evaluation criteria. The proposed design offers a structured basis for developing AI systems that remain interpretable and risk-aware as real-world conditions evolve.