AIGC and the Evolution of Intelligent Systems: From Content Generation to Adaptive Interaction

Abstract

With the rapid advancement of Artificial Intelligence Generated Content (AIGC) technology, its integration into education has opened new possibilities for intelligent and personalized teaching. This paper explores the application of AIGC in reforming the teaching model of Python programming courses. First, it analyzes the current challenges in Python teaching, including single teaching methods, insufficient use of third-party libraries, and inadequate practice design. Then, it proposes an AIGC-assisted teaching framework that encompasses case-driven theoretical instruction, personalized learning path generation, intelligent tutoring and Q&A, and real-time learning evaluation. The proposed model enables individualized learning experiences by leveraging AIGC’s capacity for adaptive content generation and intelligent feedback, thus improving students’ engagement, programming competence, and problem-solving abilities. Finally, the paper discusses the outlook and challenges of applying AIGC in programming education, emphasizing the need to address issues of data privacy, model stability, and content quality. The findings provide theoretical guidance and practical reference for building intelligent, efficient, and inclusive programming education systems in the digital era.