Research on the Teaching Practice of AI Teaching Assistants in C Language Programming Courses

Abstract

Under the traditional teaching model, C language programming has long faced practical pain points such as high entry barriers for beginners, delayed feedback on programming practice, insufficient supply of tiered teaching, and excessive teaching burden on frontline teachers. This study, with the core support of the human-machine collaborative education concept, explores the normalized implementation path of deeply embedding AI teaching assistants into the entire process of programming classes, and constructs a ternary collaborative teaching framework of "teacher guidance, AI teaching assistance, and student subjectivity". The study simultaneously sorts out practical issues existing in current AI teaching applications, such as superficial problem-solving, excessive dependence of students on technical tools, and insufficient adaptability to curriculum scenarios, and proposes targeted optimization strategies accordingly. Practice has shown that this teaching model can effectively compensate for the shortcomings of traditional classrooms, providing a practical reference for improving the intelligent teaching quality of computer programming courses in universities.