Revolutionizing education by introducing innovative methods to enhance student experiences has birthed Artificial Intelligence (AI). This article provided an in-depth overview of AI's educative and transformative influence, particularly concentrating on learning outcomes for students of all ages at Kumasi Technical University. AI amalgamation in education has enabled modified learning experiences tailored towards each learner's unique needs. The purpose of this study sought to investigate the effects of AI-personalized learning systems on academic performance across different age groups in higher education institution. The researcher employed a quantitative research design, using a face-content verified structured questionnaire to collect data from respondents, with expert consultation. Forty-five students from Kumasi Technical University's engineering and procurement departments were selected using the convenience sampling technique. The findings provided valuable insights into the use of AI-driven personalized learning platforms in higher education. The data revealed higher adoption rates among undergraduates compared to postgraduates, and a greater likelihood of use among men than women, highlighting gender disparities and potential areas for targeted support. The predominant use of AI tools by younger students demonstrated their comfort with emerging technology, while the low participation of older students suggested potential adoption barriers. Statistical analyses (Pearson correlation; (r (43) = 0.166, p = 0.265) and linear regression; (R^2 of 0.03), (F (1, 45) = 1.25, p = 0.265) indicated that age did not significantly correlate with academic success in the context of AI use, despite extensive integration of AI learning systems in academic courses. Contrary to expectations that younger students' engagement with AI tailored learning systems would positively impact their academic performance compared to those over thirty, no significant correlation between age and academic achievement was found. These findings underscore the need for further research into other factors that may influence the effectiveness of AI learning systems.
