Intelligent Teaching Assistants Combining Computer Vision and Natural Language Processing: Enhancing Personalized Tutoring in Online Education

This study explores the potential of Intelligent Teaching Assistants (ITAs) that utilize computer vision (CV) and natural language processing (NLP) technologies to improve personalized educational experiences in online learning environments. The ITA system uses computer vision to track students' facial expressions and physical stance together with eye movement while employing natural language processing to interpret spoken and written student questions for customized responses. The research details the ITA system's structural design which encompasses data gathering and real-time adaptability functions while assessing its effects on student engagement levels and academic performance and satisfaction measures. The two-week study with 50 students showed better learning results and higher engagement than standard online teaching techniques. The ITA system reached 85% student engagement by the end of the experiment and quiz scores increased by 20%. After conducting surveys post-experiment students exhibited high satisfaction levels because personalized feedback and adaptive responses enhanced their learning experience. The research reveals how AI-based ITAs could transform online education with their ability to provide scalable learning solutions that are customized for each student.