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Ai-powered Learning Analytics and Academic Performance Prediction

Year: 2026
Type: article
Abstract: Artificial Intelligence (AI) has become a transformative force in the education sector, particularly through learning analytics and predictive modeling. AI-powered learning analytics refers to the use of data-driven techniques to collect, analyze, and interpret student learning behaviors in order to improve academic outcomes. This research paper explores the role of AI in predicting academic performance and enhancing decision-making in educational environments. By leveraging machine learning algorithms, educational institutions can identify at-risk students, personalize learning experiences, and improve retention rates. The study highlights how AI systems analyze large volumes of data such as attendance, assignment submissions, quiz scores, engagement patterns, and interaction with learning platforms. Predictive models use this data to forecast student performance and recommend timely interventions. The paper also examines the benefits of AI-based analytics, including improved academic support, early warning systems, and enhanced teaching strategies. However, challenges such as data privacy concerns, algorithmic bias, and lack of transparency in AI models remain significant barriers to implementation. Ethical considerations must be addressed to ensure fair and responsible use of student data. The findings suggest that AI-powered learning analytics can significantly improve academic performance prediction and educational outcomes when implemented responsibly. The study concludes that integrating AI into educational systems can lead to a more personalized, efficient, and data-informed learning environment. Keywords: Artificial Intelligence, Learning Analytics, Academic Performance, Predictive Modeling, Education Technology.
Language: English
Open Access status: diamond
OpenAlex ID: W7218277424
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