The primary aim of this chapter is to present case studies demonstrating the application of Interactive Machine Learning (IML) in creating Adaptive Learning Technology Systems. By leveraging the synergy between machine learning algorithms and Human–Computer Interaction (HCI), these systems offer personalized, dynamic, and scalable educational experiences tailored to individual learners. The chapter explores three case studies: a personalized Java tutoring platform, a language learning assistant, and an adaptive science curriculum. These examples illustrate how IML enables the customization of content, feedback, and learning pathways, enhancing engagement and improving learning outcomes. Emphasis is placed on the integration of real-time feedback loops and personalized recommendations, ensuring alignment with diverse cognitive styles and learner progress. The chapter also highlights collaborative features and their role in fostering inclusivity and shared knowledge within digital learning ecosystems. Additionally, challenges such as data privacy and system scalability are addressed, providing a comprehensive overview of IML’s potential to revolutionize education. This chapter underscores the role of IML in shaping the future of adaptive learning systems, offering valuable insights for researchers and practitioners.