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Applied Artificial Intelligence for Predictive Student Performance and Early Academic Intervention in Saudi Secondary Schools
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Predictive analytics in the field of education has moved from providing retrospective descriptions to the use of applied artificial intelligence systems that are able to identify academic at-risk students while there is still time to take action. This review looks at the ways in which that shift can be implemented in Saudi secondary schools, stressing that early identification will only be worthwhile if prediction is linked to fair, interpretable and privacy-respecting interventions. It draws together research published between 2020 and 2025 on educational data mining, machine learning, deep learning, early warning systems, explainable artificial intelligence, and the adoption of artificial intelligence in K-12 education, as well as Saudi policy documents relating to the development of human skills and personal data. Instead of focusing on which classifier achieves the highest individual accuracy, the analysis addresses five implementation questions: which predictors are actionable, when predictions become reliable enough to trigger intervention, how model effectiveness should be validated, how teachers and counselors should make use of the explanations, and what kind of governance is needed regarding data from minors. It is consistently found that previous achievement and continuous assessment are strong predictors, while attendance, engagement, traces from the learning-management system, and certain contextual variables can provide additional useful information. Although ensemble learning and deep models might improve discrimination in certain datasets, their usefulness depends on temporal validation, calibration, impartiality across subgroups, interpretability, and incorporation into everyday operations. A framework customized to the Saudi context is suggested in which school data go through privacy and quality checks, the models produce calibrated risk estimates, the explanations assist professional judgment, and tiered interventions are recorded and evaluated. The review concludes by stating that the most defensible aim of applied artificial intelligence in secondary education should not be automated student classification, but rather earlier, more consistent and auditable human support.
Keywords
educational data mining; predictive analytics; early warning systems; machine learning; secondary education; Saudi Arabia; explainable AI; academic intervention
How to cite this paper
@article{1722647,
author = {Abdul Rehman Barok},
title = {Applied Artificial Intelligence for Predictive Student Performance and Early Academic Intervention in Saudi Secondary Schools},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {2},
pages = {3584-3596},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722647.pdf},
abstract = {Predictive analytics in the field of education has moved from providing retrospective descriptions to the use of applied artificial intelligence systems that are able to identify academic at-risk students while there is still time to take action. This review looks at the ways in which that shift can be implemented in Saudi secondary schools, stressing that early identification will only be worthwhile if prediction is linked to fair, interpretable and privacy-respecting interventions. It draws together research published between 2020 and 2025 on educational data mining, machine learning, deep learning, early warning systems, explainable artificial intelligence, and the adoption of artificial intelligence in K-12 education, as well as Saudi policy documents relating to the development of human skills and personal data. Instead of focusing on which classifier achieves the highest individual accuracy, the analysis addresses five implementation questions: which predictors are actionable, when predictions become reliable enough to trigger intervention, how model effectiveness should be validated, how teachers and counselors should make use of the explanations, and what kind of governance is needed regarding data from minors. It is consistently found that previous achievement and continuous assessment are strong predictors, while attendance, engagement, traces from the learning-management system, and certain contextual variables can provide additional useful information. Although ensemble learning and deep models might improve discrimination in certain datasets, their usefulness depends on temporal validation, calibration, impartiality across subgroups, interpretability, and incorporation into everyday operations. A framework customized to the Saudi context is suggested in which school data go through privacy and quality checks, the models produce calibrated risk estimates, the explanations assist professional judgment, and tiered interventions are recorded and evaluated. The review concludes by stating that the most defensible aim of applied artificial intelligence in secondary education should not be automated student classification, but rather earlier, more consistent and auditable human support.},
keywords = {educational data mining; predictive analytics; early warning systems; machine learning; secondary education; Saudi Arabia; explainable AI; academic intervention},
month = {August},
}