The impact of artificial intelligence and learning analytics on students’ academic performance
Abstract
The adoption of artificial intelligence in education, learning analytics, and educational data mining has changed the contemporary learning landscape but still its real impact on the academic achievements of the students has been disjointed through various empirical studies. The growth of AI-based education, predictive analytics within education, and adaptive learning systems has created the demand to synthesize the emerging evidence systematically to establish how the technologies impact student success, engagement and learning outcomes. The literature review is based on a PRISMA framework to identify, screen, and analyze current studies on the role of artificial intelligence, learning analytics, and learning data-driven environments on academic performance in school and higher education settings. The review procedure was based on the PRISMA on the topic of machine learning in education, intelligent tutoring systems, personalized learning, and student performance prediction models. The research results show that AI adaptive systems and learning analytics dashboards can always increase academic performance by timely identifying risks, providing personalized feedback, and monitoring data, whereas educational big data allows the institutions to create specific interventions. Nevertheless, the obstacles of data privacy, algorithmic bias, excessive automation, and disparities in access to digital resources are also major obstacles. The conclusion of the review is that the collective application of Artificial Intelligence, Learning Analytics and predictive educational technologies have a high potential to improve academic performance as long as they are applied with ethical principles, pedagogical orientation and institutional endorsement.
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References
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Copyright (c) 2026 Mohd. Shuyeb, Surinder Kumar, Mukesh Kumar, Ankur Jain (Author)
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Copyright (c) 2025 International Journal of Applied Resilience and Sustainability (IJARS) 
This work is licensed under a Creative Commons Attribution 4.0 International License.