Artificial Intelligence-Based Prediction of Learning Difficulties Through Educational Data Analytics

Elena Rinaldi

Artificial Intelligence-Based Prediction of Learning Difficulties Through Educational Data Analytics

Author(s)	Elena Rinaldi
Country	Italy
Abstract	Educational institutions increasingly generate digital information through learning management systems, online assessments, attendance platforms, assignment submissions, and student interaction records. These data can contain early indicators of academic difficulty before poor final outcomes become apparent. Artificial intelligence and educational data analytics provide a methodological basis for combining multiple indicators and estimating which learners may require additional instructional support. Such prediction should be interpreted as an early-support mechanism rather than as a diagnostic classification of students. This hypothetical methodological study examines artificial intelligence-based prediction of learning difficulties through educational data analytics. A fictional dataset containing 1,800 student records and twenty-two educational variables is divided into training, validation, and independent testing subsets. Three conceptual predictive models are compared: logistic regression, support vector machine, and random forest classification. Predictor variables include assessment performance, assignment completion, learning-management-system activity, attendance consistency, response latency, repeated-error patterns, and recent changes in engagement. The illustrative analysis evaluates accuracy, precision, recall, and F1 score using a hypothetical test set containing ninety students designated as requiring additional academic support and 210 students without the defined support-risk label. Under the assigned values, the random forest model produces the strongest overall performance. A second analysis examines prediction quality at different stages of a fictional academic term and indicates that model reliability improves as additional educational evidence becomes available. The study concludes that AI-based educational analytics can support earlier recognition of learners who may benefit from instructional assistance. However, predictions should supplement teacher judgment rather than determine student capability, placement, diagnosis, or opportunity. Reliable use requires transparent labeling, representative data, privacy protection, subgroup monitoring, human review, and educationally meaningful intervention procedures. All numerical findings reported in this manuscript are illustrative rather than empirically observed.
Keywords	Artificial Intelligence, Educational Data Analytics, Learning Difficulties, Educational Data Mining, Learning Analytics, Student Performance Prediction, Early Warning Systems, Machine Learning, Academic Support, Predictive Analytics.
Field	Engineering
Published In	Volume 2, Issue 3, May-June 2026
Published On	2026-06-23

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Artificial Intelligence-Based Prediction of Learning Difficulties Through Educational Data Analytics

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