<![CDATA[Background: Complex machine learning (ML) systems often require substantial computational resources, making them difficult to deploy in real-world environments constrained by hardware limitations, interpretability requirements, and regulatory standards. While knowledge distillation (KD) has traditionally been viewed as a model compression technique, its broader implications for efficiency, interpretability, and regulatory compliance remain underexplored.Aims: This study aims to reconceptualize knowledge distillation beyond model compression by framing it as a dual strategy for efficiency and interpretability enhancement. The paper proposes a structured distillation protocol that integrates predictive performance assessment, computational profiling, and feature attribution alignment within a unified experimental design.Methods: The proposed distillation protocol employs a temperature-scaled objective function combining supervised cross-entropy loss and Kullback Leibler divergence to facilitate relational knowledge transfer from teacher to student models. Experiments were conducted across multiple benchmark datasets. Evaluation consisted of three components: (1) predictive performance measurement, (2) computational efficiency profiling including parameter counts and inference latency, and (3) interpretability analysis using feature attribution similarity and perturbation stability metrics. Statistical analyses were performed to assess performance differences.Result: Across benchmark datasets, distilled student models achieved teacher-level accuracy ranging between 95% and 98%. Parameter counts and inference latency were reduced by more than 60%. Interpretability analyses showed improved explanation consistency, smoother decision structures, and higher feature attribution alignment. Statistical testing confirmed that efficiency and interpretability gains were obtained without significant performance degradation.Conclusion: The findings support the reconceptualization of knowledge distillation as a dual optimization strategy that enhances both operational efficiency and interpretability while preserving predictive strength. Rather than serving solely as a compression mechanism, KD functions as a scalable and adaptive framework for deployment-ready AI systems that balance performance, computational constraints, and explanation stability.]]>
