Enhancing Educational Outcomes with Explainable AI: Bridging Transparency and Trust in Learning Systems
K Sai Geethanjali, Nidhi Umashankar
Abstract
Artificial Intelligence (AI) is increasingly shaping educational landscapes by facilitating personalized learning, adaptive teaching methods, and efficient assessment tools. Despite its transformative potential, the adoption of AI in education faces significant barriers, primarily due to the "black-box" nature of many AI models, where decisions are not easily interpretable. This lack of transparency often leads to reduced trust among educators, students, and stakeholders, hindering the widespread implementation of AI in educational environments. This paper explores the role of Explainable AI (XAI) in enhancing educational outcomes by fostering transparency and trust. It delves into the challenges posed by opaque AI models, presents the benefits of XAI in addressing these challenges, and proposes a framework for integrating explainability in AI-driven educational systems. Additionally, we discuss case studies that illustrate the positive impact of XAI in real-world educational settings.