Smart Education Using Explainable Artificial Intelligence

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Author(s): Nikita Sharma (Poornima University, India)
Copyright: 2025
Pages: 36
Source title: Internet of Behavior-Based Computational Intelligence for Smart Education Systems
Source Author(s)/Editor(s): Mariya Ouaissa (Cadi Ayyad University, Morocco), Mariyam Ouaissa (Chouaib Doukkali University, Morocco), Hanane Lamaazi (College of Information Technology, UAE University, UAE), Mahmoud El Hamlaoui (Mohammed V University, Morocco)and Kishor Kumar Reddy C. (Stanley College of Engineering and Technology for Women, India)
DOI: 10.4018/979-8-3693-8151-9.ch004

Keywords: Computational Intelligence / Computer Science & IT / Educational Technologies / Information Science Reference

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Abstract

Explainable AI (XAI) is revolutionizing the field of education by improving the efficacy, transparency, and trustworthiness of AI-powered solutions. The theoretical underpinnings of XAI are examined in this chapter, along with a comparison between XAI and standard AI with an emphasis on the value of interpretability in educational settings. The potential of model-agnostic approaches like SHAP, LIME, and counterfactual explanations, as well as model-specific approaches like decision trees, attention mechanisms, and linear models, to improve the interpretability of AI judgments is examined. Saliency maps and rule extraction are two examples of rule-based and visually driven explanations that help achieve this objective. The chapter ends with a discussion of the potential of XAI in promoting fair, open, and efficient AI-driven learning environments, as well as the necessity of further research to address the related issues.

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