AI-Driven Student Performance Prediction Models in EdTech

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DOI: https://doi.org/10.63345/ijre.v14.i10.3

Er. Niharika Singh

ABES Engineering College

Crossings Republik, Ghaziabad, Uttar Pradesh 201009

Abstract
Artificial intelligence (AI)–driven student performance prediction models have emerged as a transformative force in educational technology (EdTech), enabling instructors, administrators, and learners themselves to harness data-driven insights for personalized learning pathways. By leveraging advanced machine learning algorithms—ranging from decision trees and support vector machines to deep neural networks—these systems analyze a multitude of variables, including but not limited to past academic records, engagement metrics in learning management systems, socio-demographic factors, and even real-time affective indicators. The predictive outputs facilitate early identification of students who may be at risk of underperforming or dropping out, thereby empowering timely and targeted interventions. However, the journey from model development to real-world application is fraught with technical, ethical, and operational challenges. Data heterogeneity and quality issues often undermine model robustness; algorithmic opacity raises concerns about fairness and accountability; and limited user literacy regarding AI tools can inhibit adoption. This manuscript presents a comprehensive survey-based investigation involving 120 K–12 educators and 180 higher-education students, aimed at evaluating both objective performance metrics and subjective stakeholder perceptions of AI prediction models. Quantitative analysis revealed that, on average, predictive accuracy exceeds 85%, with ensemble methods and hybrid deep-learning architectures showing particular promise. Nonetheless, 74% of respondents expressed significant concerns regarding data privacy, and 68% highlighted the need for greater model explainability.

Keywords

AI, Student Performance, Prediction Models, EdTech, Machine Learning

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