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Abstract— Artificial Intelligence (AI) is increasingly shaping educational assessment, yet its opaque decision-making processes raise concerns about fairness, trust, and accountability. This paper explores the role of Human-Centered Explainable AI (XAI) in enhancing transparency and interpretability within student assessment systems. By integrating XAI frameworks into learning analytics and intelligent tutoring platforms, we demonstrate how explanations can empower educators and learners to understand, validate, and challenge algorithmic outcomes. The study highlights ethical considerations, including bias detection, equity in evaluation, and compliance with emerging regulatory standards. Through case analyses and conceptual models, we argue that human-centered XAI fosters not only technical interpretability but also pedagogical trust, positioning it as a cornerstone for responsible AI adoption in education.
"Human-Centered Explainable AI in Education Enhancing Fairness and Interpretability in Student Assessment ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.b253-b263, June-2026, Available :http://www.ijrti.org/papers/IJRTI2606139.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator