PERSONALIZED LEARNING VIA AI TUTORS: A COMPUTATIONAL PSYCHOLOGY APPROACH TO MODELING STUDENT MOTIVATION AND COGNITIVE STATES
Abstract
The integration of artificial intelligence (AI) in education has led to the development of personalized learning systems that adapt to students’ unique learning needs. However, there is limited research on how AI tutors can model and respond to students’ cognitive states and motivation. This study explores the application of computational psychology to AI-based tutoring systems, focusing on how AI can simulate student motivation and cognitive processes to enhance learning experiences. The aim of this research is to create a computational model that incorporates psychological theories of motivation and cognitive states to personalize learning through AI tutors. The model integrates concepts from cognitive psychology, such as attention, memory, and intrinsic motivation, into AI algorithms that assess and respond to students’ learning behaviors in real time. Using a dataset of student interactions with an AI tutor, we employed machine learning techniques to simulate the students’ cognitive states and predict their learning outcomes based on varying levels of motivation. The results show that AI tutors significantly improve students’ engagement and performance when personalized learning strategies are applied. This research demonstrates the potential of AI-driven personalized learning systems to foster better academic outcomes by responding dynamically to students’ psychological states. The findings offer valuable insights for future AI-based educational tools aimed at enhancing student learning experiences.
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