In this research study, we explore the impacts of AI-enabled mobile learning, which we examine with the students majoring in computer software technology at Jiangxi College of Applied Technology. Artificial intelligence (AI) has been integrated into mobile learning settings due to the newness of education methods. Such settings facilitate personalized learning experiences features, instant feedback, and dynamic content Delivery. The usefulness, however, of these advancements is highly reliant on student involvement, engagement, and self-regulated learning systems, all of which are still under -utilized. This study explores how AI-based mobile learning impacts students' learning outcomes, motivation, and behavioral transformations. The research is an experimental quantitative research. Using cluster sampling, 160 students were divided into the experimental and control groups, and data were obtained through standard questionnaires. The control group used conventional mobile learning without AI and the experimental group received mobile learning with the support of AI. SPSS was used to conduct statistical analysis to determine patterns, correlations, and variation in learning outcomes and motivation by groups. The findings are expected to deliver actionable insights that might be used to optimize AI-driven mobile learning tactics, encourage student autonomy, and improve teaching practices in the domain of computer science education. This research aims to inform educators, administrators, and policymakers concerning proper pedagogical interventions to optimize the advantages of artificial intelligence in mobile learning contexts. Achieved by filling the gaps in our knowledge of how students interact with AI tools.