1 Department of Statistics, Bowling Green State University, USA.
2 Department of STEM Education, College of Education, University of Kentucky, Lexington, USA.
World Journal of Advanced Research and Reviews, 2025, 27(01), 2658-2671
Article DOI: 10.30574/wjarr.2025.27.1.2548
Received on 11June 2025; revised on 20July 2025; accepted on 22July 2025
The integration of artificial intelligence (AI) and predictive analytics in educational and healthcare settings represents a paradigm shift in how we assess cognitive abilities and implement early interventions for STEM learning difficulties. This article examines the current landscape of AI-driven cognitive assessment tools in the United States, their applications in identifying at-risk students, and their potential for improving both educational outcomes and broader health implications. Through analysis of recent implementations across American academic institutions and healthcare systems, we demonstrate that AI-powered predictive models can identify learning difficulties with 85-92% accuracy while reducing assessment time by up to 60%. The findings suggest that early intervention programs guided by AI analytics show significant improvements in STEM performance metrics and long-term cognitive health outcomes.
Artificial Intelligence; Predictive Analytics; Cognitive Assessment; STEM Education; Early Intervention; Educational Technology
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Kamorudeen Abiola Taiwo and Isiaka Olayinka Busari. Leveraging AI-driven predictive analytics to enhance cognitive assessment and early intervention in STEM learning and health outcomes. World Journal of Advanced Research and Reviews, 2025, 27(01), 2658-2671. Article DOI: https://doi.org/10.30574/wjarr.2025.27.1.2548.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0