<![CDATA[The need to understand and tailor teaching methods to the individual needs of students is becoming increasingly important. This study focuses on the utilization of deep learning, a field within computer science that enables computers to learn from experience and understand the world in data form, to detect and analyze students' learning styles. Through an in-depth literature study, this research collects and evaluates various sources and previous studies relevant to the topic. We examine the ways in which deep learning has been applied to recognize patterns in students' learning data, such as their information absorption methods, the time they need to learn new concepts, and their learning preferences. This is assisted by data analysis taken from various sources, such as test results, learning activity reports, and student feedback. The research findings indicate that deep learning holds significant potential in supporting more personalized and responsive education. Deep learning models can be used to develop a learning system capable of adjusting learning content and teaching strategies based on each student's unique learning style. This can not only enhance learning effectiveness but also student motivation and satisfaction with the learning process. However, the study also finds that the application of deep learning in education faces challenges, including the requirement for large amounts of data for effective model training and ethical issues related to student data privacy. Collaboration between technicians, educators, and other stakeholders is required to overcome these challenges and fully leverage the potential of deep learning in education.]]>
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