Interpretable Artificial Intelligence in Pedagogical Diagnostics and Math Performance Prediction: A Systematic Review of Applications in International Standardized Assessments
DOI:
https://doi.org/10.5281/zenodo.18431440Ключевые слова:
artificial intelligence, explainable AI, pedagogical diagnostics, student success prediction, standardized tests, SAT, GRE, PISA, interpretable machine learning, transparency, ethics, SHAP, LIME, attention mechanisms, personalized feedback, trust in AI, education reform, resource allocation, at-risk student identification.Аннотация
Artificial intelligence is already delivering tangible results in predicting student success. However, a critical challenge
remains: most models function as “black boxes,” leaving teachers without a clear understanding of how decisions
are actually made. This review focuses on explainable artificial intelligence (XAI) methods for forecasting performance on
standardized assessments such as the SAT, GRE, and PISA. Specific tools–including SHAP, LIME, and attention mechanisms–
are examined. The evidence indicates that XAI significantly enhances both assessment accuracy and transparency.
Nevertheless, substantial challenges persist: explanations are often overly technical, and effective classroom
integration requires considerable preparatory work. This review systematizes practical use cases, identifies the strengths
of key methods, and maps concrete implementation barriers, offering value for stakeholders seeking to deploy AI responsibly
in educational diagnostics.
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