Large Language Models (LLMs) are reshaping the design and capabilities of intelligent tutoring systems (ITS) by providing powerful generative, reasoning and interaction abilities, which surpass traditional rule-based approaches. This survey presents a structured overview of LLM-based ITS and analyzes how these models transform classical system components and architectures. We first review the foundational concepts of traditional ITS and introduce the functional roles of the main components, followed by LLM-based techniques and related datasets for realizing each of these components. Furthermore, we examine the key application domains and concludes the survey by outlining future research directions.