Abstract
This study explored the impact of AI-powered adaptive testing on student academic fortunes and test anxiety, with a focus on the effectiveness of adaptive testing compared to traditional methods. Utilizing a quantitative research design, the study employed hierarchical regression analysis to examine how AI-powered adaptive testing influences student outcomes, accounting for factors such as demographics, prior academic performance, and familiarity with technology. The sample comprised 250 senior high school students in Ghana, selected through stratified random sampling. The results indicated that AI-powered adaptive testing significantly enhanced student academic fortunes and reduced test anxiety relative to traditional testing methods. Hierarchical regression analysis revealed that adaptive testing explained an additional 20% of the variance in student academic fortunes and 16% in test anxiety. The study also found that student demographics, prior academic performance, and familiarity with technology significantly influenced the effectiveness of adaptive testing. Normality tests confirmed the robustness of the regression results. The findings suggest that AI-powered adaptive testing offers a more personalized and effective approach to student assessment, potentially improving educational outcomes and reducing anxiety. Recommendations include integrating adaptive testing systems into educational practices, providing training for educators, addressing technological familiarity, ensuring equitable access, and conducting further research to explore long-term impacts and effectiveness in diverse educational contexts.