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A central component of many AIED systems is a “domain model,” that is, a representation of knowledge of the domain of instruction. The system uses the model in many ways to provide instruction that adapts to learners. Not all AIED systems have an elaborate domain model, but in those that do, the domain model is central to the system’s functioning. In fact, domain models fulfill so many important functions within AIED systems that entire classes of AIED systems are defined in terms of the types of domain model they use (such as model-tracing tutors, constraint-based tutors, example-tracing tutors, and issue-based approaches to build- ing tutoring systems). Across AIED projects, systems, and paradigms, the types of domain models used span the gamut of AI representations. AIED systems use their domain models for many different purposes, chief among them assessing student work, which is foundational for other functionality.
This chapter reviews major approaches to domain modeling used in AIED systems and briefly touches on the corresponding student models and the way they are used to track an individual student’s knowledge growth. (We do not discuss student models that target other aspects, such as affect, motivation, self-regulation, or metacognition.) We discuss, in turn: rule-based models, constraint-based models, Bayesian networks, machine-learned models, text-based models, generalized examples, and knowledge spaces. These types of models have been studied extensively in AIED research and have been the foundation for many AIED systems that have been proven to be effective in enhancing student learning or other aspects of the student experience. A number of these approaches are now used in AIED systems that are used on a wide scale in educational practice. The chapter discusses how these approaches support key aspects of an AIED system’s behavior and enable the system to adapt aspects of its instruction to individual student variables. We also highlight challenges that occur when applying the different approaches. We look at the use of machine learning and data-driven methods to create or refine domain models, so they better account for learning data and sup- port more effective adaptive instruction. As well, we make note of connections between a system’s domain model and other key components, including the system’s student model. We base this discussion on traditional views of intelligent tutoring systems (ITSs), which divide the system’s architecture into four main components: a domain model, a student model, a pedagogical model, and a problem-solving environment. We focus on systems that support individual learning. Other types of AIED systems are covered in other chapters.