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This book explores the transformative roles of Human-Computer Interaction (HCI) and Augmented Intelligence (AI) in shaping intelligent systems. HCI focuses on designing interactive systems that enhance human-technology relationships, while AI empowers users with adaptive, data-driven tools that complement decision-making. Together, these fields drive innovation, creating systems that are efficient, intuitive, and inclusive, addressing diverse user needs across various domains. One example of such an adaptive system is a recommendation system that suggests personalized content based on users’ preferences and interactions. To illustrate this, let’s consider a simple recommendation system using Machine Learning. We can create a Python example using the collaborative filtering approach, which relies on past interactions or preferences to make predictions about what a user might like. In this case, we’ll use the surprise library, a popular tool for building recommendation systems. We will use a dataset that contains user ratings for movies and demonstrate how the system can suggest movies that a user might be interested in based on their previous ratings and the ratings of other similar users. To begin, we will install the necessary libraries and load a dataset. Preface Human–Computer Interaction in Education The Role of Augmented Intelligence and Pedagogical Theories in Digital Learning Teaching Methods and Online Instructional Design Cognitive Styles as a Factor of Effective Learning Learning Styles: Assisting Students Towards Educational Success The Role of Communication Styles in the Learning Process Learner Modeling and Analysis Case Studies of Interactive Machine Learning for Adaptive Learning Technology Systems A Novel Framework of Human–Computer Interaction and Human-Centered Artificial Intelligence in Learning Technology