Intelligent Tutoring System

Intelligent tutoring systems (ITS) aim to personalize education by providing adaptive feedback and instruction tailored to individual student needs and learning styles. Current research heavily utilizes large language models (LLMs) and other machine learning techniques, such as Bayesian networks, hierarchical task networks, and multi-armed bandits, to create interactive systems that generate personalized content, assess student understanding, and provide targeted hints or explanations. This focus on AI-driven personalization holds significant promise for improving learning outcomes, particularly in addressing challenges like scalability in large classrooms and providing equitable access to high-quality education. Furthermore, research is actively exploring methods to improve the validity and pedagogical effectiveness of automatically generated feedback within these systems.