This chapter explores the concept of learning styles and their significant role in enhancing educational experiences. It emphasizes how understanding individual learning preferences can improve the design of educational technologies, particularly in the fields of Human–Computer Interaction (HCI) and Augmented Intelligence (AI). The chapter discusses several well-established learning style models, such as the Felder-Silverman Model, VARK, and the 4MAT model, highlighting their applications in computer-assisted learning environments. These models help tailor educational content and interactions to suit different cognitive preferences, ensuring a more personalized and engaging learning experience. By integrating AI and machine learning, educational platforms can adapt in real-time to individual learner profiles, offering targeted activities and resources that align with each student’s preferred learning style. The chapter also addresses the debates around the efficacy of learning styles, acknowledging both their potential benefits and criticisms. Despite ongoing research, the utility of learning styles in guiding instructional design remains vital for creating effective, student-centered learning environments. Ultimately, this chapter provides a comprehensive overview of how learning styles can be leveraged in modern educational technologies to foster more effective and inclusive learning experiences, enhancing engagement and improving learning outcomes.