Explainable AI: Enhancing Transparency and Trust in Educational Technology

Author

• Avishek Barman
• Anal Acharya
• Soumen Mukherjee
• Shubham Chatterjee, Missouri University of Science and TechnologyFollow
• Arpan Deyasi

Abstract

Educational institutions face growing worries about AI algorithms because of their unclear operations and unfairness and lack of responsibility in decision-making processes. XAI has materialized as an essential research domain that develops methods to enhance AI decision transparency for teaching staff and students while supporting educational administrators. The work examines how XAI benefits educational deployments while focusing on the necessity of explainable features in Teaching-Learning-Assessment (TLA) procedures. This chapter analyzes essential XAI techniques which include model-agnostic approaches represented by SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) alongside overviews of other classical techniques for inherently interpretable modeling. Student academic performance evaluations using AI-grading systems and predictive models undergo systematic investigation throughout this research project. The paper performs a review of different explainability methods to evaluate their ability to enhance model clarity without diminishing predictive precision. The research investigates how XAI helps reduce biases in automated educational systems by creating unbiased grading and admission procedures. The research establishes through experiments that explainable models along with explanatory methods strengthen user faith when AI systems handle educational choices. This research finds wide-reaching implications for both educational institutions through their educators and policymakers as well as AI developers who need to adopt explainability frameworks into their AI educational tools. XAI enhances trust by making AI-driven recommendations transparent which allows for informed decision-making and enables ethical adoption of AI systems in education. Research results emphasize the need for AI to find equilibrium between prediction success and interpretability to deliver improved learning results with sustained fairness and accountability.

Recommended Citation

A. Barman et al., "Explainable AI: Enhancing Transparency and Trust in Educational Technology," Machine Learning Based Approaches for Pedagogical Data Analysis, pp. 222 - 241, Taylor and Francis Group, Taylor and Francis, Jan 2026.

The definitive version is available at https://doi.org/10.1201/9781003559511-12

Department(s)

Computer Science

International Standard Book Number (ISBN)

978-103287190-5;978-104058962-5

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Taylor and Francis Group; Taylor and Francis, All rights reserved.

Publication Date

01 Jan 2026