Multimodal Learning Behavior Analytics and Personalized Pathway Planning for AI Teaching Assistants in Higher Education

Abstract

In the context of educational digital transformation, AI teaching assistants in higher education serve as the key medium for achieving personalized teaching. This study reviews the methodological frameworks, technical means and application status of multimodal learning
behavior analytics in the application of AI teaching assistants in higher education, explores the principles, methods and challenges of personalized learning pathway planning, summarizes the shortcomings of existing research results and presents forward-looking insights on future
trends, aiming to provide theoretical reference and operational models for the improvement and update of AI teaching assistants in higher education and the implementation of personalized teaching.