Interpretable Artificial Intelligence in Pedagogical Diagnostics and Math Performance Prediction: A Systematic Review of Applications in International Standardized Assessments

Dushabaeva, Diana

Мактабгача ва мактаб таълими · 2026-yil

Annotatsiya

Artificial intelligence is already delivering tangible results in predicting student success. However, a critical challengeremains: most models function as “black boxes,” leaving teachers without a clear understanding of how decisionsare actually made. This review focuses on explainable artificial intelligence (XAI) methods for forecasting performance onstandardized 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 classroomintegration requires considerable preparatory work. This review systematizes practical use cases, identifies the strengthsof key methods, and maps concrete implementation barriers, offering value for stakeholders seeking to deploy AI responsiblyin educational diagnostics.

Maqola ma’lumotlari
MualliflarDushabaeva, Diana
JurnalМактабгача ва мактаб таълими
Nashr sanasi2026-01-20
Jild4
Son1
TilIngliz
DOI10.5281/zenodo.18431440

Kalit so‘zlar

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.

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