Domain modeling for AIED systems with connections to modeling student knowledge: a review

This chapter reviews major approaches to domain modeling used in AIED systems. We consider the many purposes for which AIED systems use their domain model, all in the service of providing effective instruction that adapts to a range of student characteristics. We briefly note connections with student modeling approaches used for tracking students’ knowledge growth. We highlight relative strengths and weaknesses of key AIED paradigms, namely rule-based models, constraint-based models, Bayesian networks, machine-learned models, text-based models, generalized examples, and knowledge spaces. We also look at the use of machine learning and data-driven methods to create or refine domain or student models, so they better account for learning data and support more effective adaptive instruction. Furthermore, we discuss relationships between a system’s domain model and its student model and pedagogical model, which are central components of many AIED systems.