Development and Application of Intelligent Tutoring System Based on AI-assisted Robots

To enhance the quality of classroom teaching in vocational schools, particularly the students' engagement during classes, this study proposes a solution for the development of an intelligent tutoring system supported by AI-assisted robots. Taking the automotive drive shaft as a case study, we focus on the concept of polar moment of inertia. The research consists of three key steps. First, we determined the dimensional parameters of the automotive drive shaft and calculated the parameters for both hollow and solid shafts using mathematical calculus formulas. Second, we incorporated the concept of polar moment of inertia into the teaching content and developed an intelligent tutoring system using Python, based on the Qwen API. Third, we conducted teaching experiments and evaluations. The results indicated that the experimental group (students using the intelligent tutoring system) improved their unit test scores by an average of 0.66 points (out of a maximum of 10) compared to the control group (students not using the intelligent tutoring system). Additionally, engagement scores in the experimental group increased by 1.29 points (out of a maximum of 10). At a significance level of 0.05 (two-tailed), there were significant differences between the experimental and control groups regarding both unit test and engagement scores. This study demonstrates the positive impact of the Qwen-based AI robot on students' learning. There is an urgent need to prioritize the development of more AI-based intelligent tutoring systems to further enhance the overall educational quality in vocational schools.