EXPLAINABLE AI FOR AUTOMATED ASSESSMENT: IMPROVING TRANSPARENCY AND TRUST IN COMPUTER-ASSISTED GRADING SYSTEMS

https://doi.org/10.5281/zenodo.17555774

Abstract

This paper discusses the potential of Explainable Artificial Intelligence (XAI) to increase transparency, fairness and trust in computer-assisted grading systems, especially in the changing educational environment of Pakistan. The study used a quantitative methodology that relied on the analysis of secondary data and synthesizing the results of published empirical studies in the period of 2018-2025. Descriptive and inferential statistics were used to compare traditional AIs models with explainable AI systems on five performance indicators including accuracy, fairness, transparency, user trust and intent to adopt. The findings indicated that XAI models are much better than traditional AI, even with greater accuracy (90% vs. 82%) and fairness (88% vs. 76), not to mention the enhancement of transparency (4.3 vs. 2.7) and user trust (4.1 vs. 2.9). The adoption intent of XAI (84%), was significantly higher, which reflects higher institutional acceptance potential. The results evidenced that explainability does not only boost technical performance but also instil user confidence and moral responsibility in automated tests. XAI provides a disruptive tool in the educational setting of Pakistan to provide equitable and transparent digital assessment. The article suggests the implementation of explainable AI systems in education policies, educator training, and EdTech partnerships to facilitate responsible and credible AI usage.

Keywords: Explainable Artificial Intelligence, Automated Assessment, Transparency, Trust, Fairness, Pakistan, Educational Technology