Exploration of AI-Driven Teaching Paradigms for Machine Learning Courses

Abstract

Machine learning courses combine theoretical depth with practical complexity,while traditional teaching models have evident limitations in addressing student diversity,engineering training, and the cultivation of innovative thinking. Grounded in the deep integration of AI with education, this paper proposes a comprehensive reform framework aimed at cultivating machine learning talent who are 'theoretically grounded, practically proficient and innovation-oriented. 'By synergistically integrating four core modules-curriculum reconstruction,pedagogical optimization, experimental paradigm enhancement, and assessment reform-and implementing a closed-loop' pre-class, in-class, post-class, and summary' workflow, this approach embeds AI across the entire learning lifecycle. The result is a systematic and goal-oriented teaching model that ensures continuous improvement and personalized learning."