The Role of Explainable AI (XAI) In Enhancing Transparency and Trust in NLP-Powered Educational Systems

As Artificial Intelligence (AI) systems become increasingly integrated into educational settings, the demand for transparency and trustworthiness has grown. Natural Language Processing (NLP)-powered applications such as intelligent tutoring systems, automated essay scoring, and educational chatbots offer significant benefits for personalized learning, yet often operate as “black boxes.” The lack of explainability in these models can undermine user trust, raise ethical concerns, and limit their effective use in classrooms. Explainable Artificial Intelligence (XAI) offers a critical solution by making AI decisions interpretable and justifiable to end-users. This review explores the role of XAI in enhancing transparency and trust within NLP-powered educational systems. It examines core challenges faced by educators and learners when using opaque AI, including bias, accountability, and adoption resistance. The paper reviews XAI techniques such as feature attribution, attention visualization, and open learner models that provide insights into model behavior. Real-world applications like the iRead literacy tutor and AI chatbots for feedback analysis illustrate how XAI can improve stakeholder confidence and system usability. The paper also outlines future research directions, emphasizing the need for user-centered explanations, multimodal transparency, and standardized evaluation frameworks. Ultimately, the integration of XAI into educational NLP tools is not merely a technical enhancement—it is essential for building ethical, effective, and human-aligned AI systems in education. By making AI outputs understandable and actionable, XAI bridges the gap between powerful algorithms and pedagogical trust.