Advancing Modern Education through Explainable Artificial Intelligence: Enhancing Learning Outcomes and Teaching with Transparent AI Systems

Abstract

The incorporation of Explainable Artificial Intelligence (XAI) into contemporary teaching presents a revolutionary paradigm designed to improve learning challenges and empower instructors while simultaneously responding to ethical concerns surrounding AI implementation. As educational technologies powered by AI, such as intelligent tutoring systems, adaptive learning systems, and automated grading software, increasingly dominate educational environments, doubts concerning transparency, trustworthiness, and accountability have become more pronounced. This research looks at current applications of XAI in the education environment and highlights approaches that enable interpretable explanations of AI-powered decisions to enhance comprehension and trust among students, teachers, and administrators. Using a sample of 200 students, the study designed AI models to forecast student pass-fail outcomes, with attendance, assignment grades, and test grades as predictor variables. The models showed excellent performance indices, with 80% accuracy, 85% precision, 77% recall, and an F1 value of 81, indicating the predictive trustworthiness of the method. The use of naturally interpretable models, such as decision trees, was supplemented with post-hoc, model-agnostic explanation techniques, including SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations), to enhance interpretability. The tools discussed yielded fine-grained insights into feature contributions, rendering it feasible to allow instructors to know AI projects in depth and align student interventions. Moral aspects, i.e., anonymisation of data, bias identification, and verification of fairness, were carried out with utmost care, resulting in a 20% reduction of the difference of prediction between demographically-defined groups. Comments received from instructors underlined appreciation of additional trust, engagement, and informed decision-making against insightful AI intelligence. The combination of innovations that are at once technical and human-centric demonstrates how XAI enables fair, transparent, and efficient educational practice. The findings add to the growing scholarship on the same.