Predicting Student Success Using Artificial Intelligence: DataDriven Approaches for Early Intervention

Session

Computer Science and Communication Engineering

Description

Artificial Intelligence (AI) is increasingly being applied in the field of education to enhance student outcomes and support personalized learning. This research explores the use of AI algorithms to predict student success by analyzing diverse data sources, including academic performance, attendance, engagement metrics, and socio-demographic factors. Machine learning models, such as decision trees, neural networks, and ensemble methods, are employed to identify students who may be at risk of underperforming or require additional support. The study evaluates the accuracy and effectiveness of these models in forecasting academic performance and highlights the potential of AI-driven interventions to provide timely assistance, improve retention rates, and promote equitable learning opportunities. Furthermore, the research discusses challenges related to data privacy, ethical considerations, and model interpretability, emphasizing the importance of responsible AI implementation in educational settings. By leveraging predictive analytics, AI can empower educators to make informed decisions, tailor instructional strategies, and ultimately enhance student achievement.

Keywords:

AI in education, student success prediction, machine learning, personalized learning

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-982-41-2

Location

UBT Kampus, Lipjan

Start Date

25-10-2025 9:00 AM

End Date

26-10-2025 6:00 PM

DOI

10.33107/ubt-ic.2025.71

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Oct 25th, 9:00 AM Oct 26th, 6:00 PM

Predicting Student Success Using Artificial Intelligence: DataDriven Approaches for Early Intervention

UBT Kampus, Lipjan

Artificial Intelligence (AI) is increasingly being applied in the field of education to enhance student outcomes and support personalized learning. This research explores the use of AI algorithms to predict student success by analyzing diverse data sources, including academic performance, attendance, engagement metrics, and socio-demographic factors. Machine learning models, such as decision trees, neural networks, and ensemble methods, are employed to identify students who may be at risk of underperforming or require additional support. The study evaluates the accuracy and effectiveness of these models in forecasting academic performance and highlights the potential of AI-driven interventions to provide timely assistance, improve retention rates, and promote equitable learning opportunities. Furthermore, the research discusses challenges related to data privacy, ethical considerations, and model interpretability, emphasizing the importance of responsible AI implementation in educational settings. By leveraging predictive analytics, AI can empower educators to make informed decisions, tailor instructional strategies, and ultimately enhance student achievement.