<![CDATA[This research emerged from the issue of the limited effectiveness of traditional learning approaches in improving students’ academic performance. The primary goal of this study is to quantitatively examine how far the integration of Artificial Intelligence (AI)–based learning methods can enhance students’ learning outcomes compared to conventional lecture techniques. A quantitative experimental design utilizing a pretest–posttest model was applied. Data were obtained through pretests and posttests and further analyzed using the Paired Sample t-test and Analysis of Covariance (ANCOVA) to determine differences and measure the influence of learning methods on achievement levels. The results indicate a substantial improvement between the pretest and posttest scores, with a mean difference of -15.167 (t = -9.575; p < 0.001), confirming that posttest scores significantly increased after the intervention. The ANCOVA results further demonstrated that the applied learning method had a strong influence on posttest outcomes (F = 35.980; p < 0.001), while the pretest variable showed no significant effect (p = 0.497). Participants who engaged in AI-integrated learning achieved an average posttest score of 85.838, notably higher than the lecture group’s 75.729, with a 95% confidence interval verifying this difference. These findings emphasize that AI integration enhances learning personalization, comprehension, and engagement, thereby contributing positively to the overall improvement of learning outcomes compared to traditional methods. The implications of this research underscore the importance of educational institutions integrating AI-based learning to enhance the quality of the learning process, support more accurate pedagogical decision-making, strengthen curriculum policy development, and promote the systematic, sustainable, inclusive, and adaptive implementation of teacher training.]]>
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