An explainable fairness-aware AI framework for exam score verification

Ensuring fairness and reliabilityin examination scoring remains a persistent challenge in educational assessment, particularly in the presence of subjective grading inconsistencies across evaluators. While automated essay scoring systems improve scalability, limited attention has been given to mechanisms for verifying whether assigned scores are consistent with rubric criteria and comparable peer responses. This study proposes an Explainable Fairness-Aware AI Framework for Exam Score Verification, designed to detect potential scoring anomalies while providing interpretable evidence to support verification decisions. The proposed framework integrates three complementary components: semantic response evaluation using contextual representations derived from DeBERTa-v3 embeddings, criterion-level rubric alignment via cross-attention mechanisms, and peer-consistency analysis based on similarity-driven cohort score distributions. To enhance transparency, an explainability layer combines attention visualization, integrated gradients, and natural language justification to provide both quantitative and qualitative insights into model decisions. The framework was evaluated on the ASAP 2.0 dataset comprising 24,728 rubric-scored essays. Experimental results, reported as mean ± standard deviation over multiple runs, demonstrate that the proposed approach achieves an agreement of 87.1% ± 0.9 with peer-consistent reference scores and a fairness consistency index of 0.82 ± 0.02, outperforming a diverse set of baseline models, including traditional, transformer-based, and hybrid scoring approaches. Ablation studies further confirm the critical role of peer-consistency analysis in detecting scoring irregularities, while the explainability components enhance interpretability without significantly affecting predictive performance. The notion of fairness addressed in this work is grounded in score consistency relative to rubric expectations and semantically similar peer responses. Within this scope, the results indicate that integrating semantic evaluation, rubric-aware modeling, and cohort-referenced analysis provides a robust and interpretable framework for exam score verification. These findings highlight the potential of multi-evidence, explainable AI systems to support reliable and transparent assessment in educational settings.