Breakthroughs in Dilemmas and Practical Approaches to the Intelligent Teaching Reform of University General AI Courses

Abstract

Artificial intelligence (AI) is reshaping social production and lifestyles, making AI literacy an essential core competency for college students in the new era. Currently, AI general education is rapidly spreading in domestic universities, but traditional models suffer from pain points such as insufficient content adaptability, weak interactivity, and singular evaluation methods, making it difficult to cater to the personalized needs of students from different majors. This study closely aligns with the digital education strategy, constructing a three-tier progressive teaching system of "basic cognition - scene integration - innovative practice". Relying on tools such as AI teaching assistants and knowledge graphs, it creates a full-process intelligent teaching paradigm, supported by a multi-dimensional guarantee mechanism. Practical verification shows that this reform model can effectively break down disciplinary barriers, significantly enhance students' interest in AI learning and practical application abilities across different majors, and provide a replicable and scalable practical path for the high-quality development of AI general education in universities.