Bridging Theory and Application: A Review of Explainable AI with a Case Study in Adaptive Learning Systems

This analysis delves into the current landscape of Explainable Artificial Intelligence (XAI), examining recent advancements, existing challenges, and prospective developments. To achieve this, the study surveys a breadth of literature on XAI, encompassing various topics such as techniques, strategies, and real-world applications. Initially, it provides an overview of XAI, encompassing its definition, historical context, and underlying motivations. Subsequently, it scrutinizes the current status of XAI, along with elucidating approaches to explainability. This involves an exploration of model-based, post-hoc, and interactive explainability techniques, alongside the methodologies employed, such as feature attribution, rule extraction, and counterfactual analysis. Furthermore, it investigates XAI’s application across diverse domains including healthcare, finance, and autonomous systems. Ethical and societal implications of XAI, including issues related to bias, accountability, and transparency, are also examined. The review identifies several challenges confronting XAI, such as the absence of standardized metrics and evaluation protocols, interpretable datasets, and domain-specific expertise. Additionally, it highlights emerging trends, recurring patterns, and potential avenues for enhancement. Ultimately, the review offers recommendations for future XAI research and development endeavors. By providing insights into the current state, limitations, challenges, and prospective directions of XAI, this analysis aims to benefit researchers, practitioners, and policymakers in navigating the field effectively.