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Abstract: Personalized instruction aims to provide learners with support that adapts to their individual knowledge. Discovering and tracing Knowledge Components (KCs) is an important step in building accurate models of student learning. However, KC discovery in computer science education is challenging due to the open-ended nature of programming, wide variability in student solutions, and intertwined use of programming structures in code. We address these challenges with a pattern-based KC discovery method that uses a data-driven approach to define KCs as recurring structural patterns in student code that reveal persistent patterns of struggle and mastery in students' solutions. We then evaluate the discovered KCs using expert evaluation and statistical student modeling to demonstrate their effectiveness in capturing student learning and struggles. We propose a framework for modeling students' learning by deriving pattern-based KCs from student code through a three-stage process. First, an attention-based code representation model identifies Abstract Syntax Tree subtrees most relevant to code correctness. Second, a Variational Autoencoder abstracts these subtrees into a smooth latent space, capturing structural similarity across student submissions. Third, the resulting representations are clustered into pattern-based KCs. To assess the effectiveness of pattern-based KCs for modeling students' learning, we adapt the Deep Knowledge Tracing model to incorporate these KCs, demonstrating significant improvements in predictive performance over baseline KT methods. Additionally, the learning curve analysis showed alignment between the derived KCs and learning theory. 

Textbooks have evolved over the last several decades in many aspects. Many textbooks can be accessed online, some of them freely. They often come with libraries of supplementary educational resources or online educational services built on top of them. These developments encouraged researchers working on different Artificial Intelligence (AI) technology to explore how AI can enhance digital textbooks and learners’ interaction with them. Over the years, research teams all over the world attempted to answer several important research questions in the new area of intelligent textbooks. How can we use AI technology to facilitate access to digital textbooks and improve the learning process? What new insights about knowledge and learning can be extracted from textbook content and data-mined from the logs of students interacting with it? How can these insights be leveraged to develop improved intelligent textbooks? How can we leverage generative AI technology to manage and augment textbooks? This special issue brings together a collection of papers that report results or recent research in the field of intelligent textbooks. These papers address the challenge of making textbooks ``more intelligent'' from different perspectives and make a step towards establishing intelligent textbooks as a new, interdisciplinary research field. 

Textbooks have evolved over the last several decades in many aspects. Most textbooks can be accessed online, many of them freely. They often come with libraries of supplementary educational resources or online educational services built on top of them. As a result of these enrichments, new research challenges and opportunities emerge that call for the application of AIED methods to enhance digital textbooks and learners’ interaction with them. Therefore, we ask: How can we use intelligent and adaptive technologies to facilitate access to digital textbooks and improve the learning process? What new insights about knowledge and learning can be extracted from textbook content and data-mined from the logs of students interacting with it? How can these insights be leveraged to develop improved intelligent texts? How can we leverage new language technology to manage and augment textbooks? This workshop will feature research contributions addressing these and other research questions related to intelligent textbooks. It will bring together researchers working on different aspects of learning technologies to establish intelligent textbooks as a new, interdisciplinary research field.