Beyond the Score: A Systematic Literature Review of Explainable Artificial Intelligence Frameworks for Promoting Equity in Automated Educational Assessment

Marites Dahilog Habagat; Leslyn Reazol

doi:10.36948/ijfmr.2026.v08i03.81089

Beyond the Score: A Systematic Literature Review of Explainable Artificial Intelligence Frameworks for Promoting Equity in Automated Educational Assessment

Author(s)	Ms. Marites Dahilog Habagat, Leslyn Reazol
Country	Philippines
Abstract	The application of Artificial Intelligence (AI) in educational assessment has revolutionized the way educational institutions grade and provide feedback. Automated assessment systems promise significant gains in efficiency and scalability, but concerns have been raised about transparency, fairness, and algorithmic bias. In particular, concerns have been raised about the extent to which learners and educators can understand, justify and contest automated decisions when opaque ‘black box’ models are used. This work is a systematic literature review of Explainable Artificial Intelligence (XAI) frameworks and their potential to support the development of equity in automated educational assessment systems . We systematically searched peer reviewed studies from 2020 to 2025, using pre-defined inclusion and exclusion criteria. Through a rigorous screening process 25 studies were selected for detailed analysis. Their findings suggest that historical training data can encode algorithmic bias within automated assessment models, which can marginalize learners from diverse linguistic, cultural and socioeconomic backgrounds. Moreover, the application of XAI techniques, such as visualization of feature importance, decision trees and explanation interfaces, was found to further enhance the transparency and trust of the stakeholders. We however find that explainability is not sufficient for fairness. The literature places a great deal of emphasis on Human-in-the-Loop (HITL) frameworks, where decision-making authority remains with educators, and AI systems function as decision support tools. The authors conclude that successful implementation of the AI-assisted assessment depends on a combination of technical transparency, ethical governance and human oversight. Educational institutions, especially in the Philippine context, are recommended to promote equitable and accountable AI use in assessment practices.
Keywords	Explainable Artificial Intelligence, Automated Assessment, Educational Equity, Algorithmic Bias, Human-in-the-Loop, Educational Technology
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-06-11
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.81089

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Beyond the Score: A Systematic Literature Review of Explainable Artificial Intelligence Frameworks for Promoting Equity in Automated Educational Assessment

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