An Intelligent Teaching Assistant System for Enhanced Online Engineering Education: A Dual‐Teacher Model

ABSTRACT As online education rapidly grows, traditional engineering education faces challenges such as limited resources, lack of interaction, and insufficient personalized support. This study proposes an Intelligent Teaching Assistant (ITA) system integrated with AI, applied in a “Dual‐Teacher” online model, to enhance teaching effectiveness and student experience in engineering courses. This study assesses the ITA system's application in the “Dual‐Teacher” model. We hypothesize that the system can improve students' learning experience, engagement, self‐efficacy, and academic performance by providing personalized support, real‐time Q&A, and emotional feedback. This study designed the ITA system architecture based on an engineering student needs survey and developed the “ChatZJU” ITA system, which was tested in the “Computer Architecture” course. A total of 80 third‐year undergraduates were randomly assigned to the experimental group (“Dual‐Teacher” model with ITA system support) and the control group (traditional teaching model). Data were collected through questionnaires, academic performance records, and engagement metrics, and were analyzed using statistical methods analysis. The experimental group showed significantly improved learning experience, engagement, and academic performance. The ITA system's personalized learning paths, Q&A, and emotional support enhanced motivation and participation. The ITA system effectively addresses the limitations of traditional online courses, improving student outcomes. Future research will focus on refining algorithms, expanding applications, and enhancing emotional support features.