<![CDATA[The rapid advancement of Artificial Intelligence (AI) has significantly trans- formed educational paradigms, particularly in adaptive learning environments where real-time personalization and intelligent feedback are essential. This study aims to explore how AI-driven mechanisms can enhance adaptive learning within learning factory environments by utilizing data analytics to personalize learning processes and optimize instructional delivery. Employing a quantita- tive research design, the data collection process involved distributing question- naires to 200 university students enrolled in AI-supported learning factory pro- grams. From this distribution, 120 valid responses were successfully obtained and analyzed, consisting of 80 students and 40 instructors across three universi- ties, representing the final usable dataset for this study. Statistical analysis was performed using regression and correlation models to assess the impact of AI- based adaptivity on learning performance, engagement, and cognitive retention. The findings reveal that AI integration within learning factories leads to sig- nificant improvements in learner adaptability, interaction efficiency, and overall academic achievement. The adaptive AI models dynamically adjusted learning content based on individual performance metrics, resulting in higher engage- ment rates and enhanced skill mastery compared to traditional non-AI-based environments. The outcomes confirm that AI can function as a critical enabler of responsive and data-driven education by bridging theoretical and practical as- pects of industrial learning. This research underscores the transformative poten- tial of Artificial Intelligence in reshaping adaptive learning environments within learning factories, emphasizing the need for further development of AI systems that prioritize personalization, continuous assessment, and the seamless integra- tion of human and machine intelligence]]>
