Enhancing Academic Achievement in Higher Education Through Adaptive Learning Algorithms

In the context of rapid technological advancement and increasingly diverse student populations, higher education institutions face mounting pressure to improve learning outcomes through innovative instructional methods. This study aims to investigate the effectiveness of adaptive learning algorithms an application of artificial intelligence in enhancing academic achievement in university level education. Focusing on students enrolled in engineering and computer science programs, a quantitative quasi experimental design was employed to compare the academic performance of an experimental group utilizing AI-powered learning tools with a control group receiving traditional instruction. Data were analyzed using Structural Equation Modeling (SEM) with SmartPLS to evaluate the relationships among key constructs, including learning motivation, student engagement, content comprehension, and academic performance. The results demonstrate that the integration of adaptive algorithms significantly enhances student engagement and comprehension, which in turn positively influences academic outcomes. Path coefficients and model reliability indices confirm the statistical robustness and validity of the findings. The study concludes that adaptive learning technologies hold considerable potential to support personalized, data driven education, offering a scalable solution to improve learning effectiveness across diverse academic contexts. These findings suggest that higher education institutions should consider broader adoption of AI-based instructional systems to foster more inclusive, efficient, and outcome oriented learning environments.