Artificial intelligence (AI) is increasingly adopted in dental education, particularly within assessment systems supporting competency-based curricula, digital workflows, and large student cohorts. Although AI-enabled assessment offers advantages in standardization, efficiency, feedback provision, and learning analytics, its educational value depends on alignment with pedagogy and professional accountability. This chapter aims to critically examine the role of AI-based assessment in undergraduate dental education using a learning-domain framework. Briefly, analysis across the cognitive, psychomotor, and affective domains clarifies where AI-supported assessment is educationally appropriate and where its contribution is intrinsically constrained. AI aligns most strongly with structured cognitive tasks and preclinical psychomotor skill development, where performance criteria can be explicitly defined and measured. In contrast, assessment of professionalism, ethical reasoning, and contextual clinical judgment remains fundamentally human-led due to its interpretive and value-laden nature. At a system level, AI-supported assessment can enhance objectivity, consistency, formative feedback, scalability, and educational intelligence. However, uncritical implementation introduces risks including superficial learning, construct under-representation, algorithmic bias, diffusion of responsibility, increased faculty interpretive burden, and erosion of learner trust. AI is therefore best positioned as a decision-support tool embedded within educator-led governance frameworks rather than as an autonomous assessor. This analysis offers a pedagogically grounded basis for integrating AI into dental assessment systems while preserving professional judgment and assessment integrity.