Exploration of AI-Driven BOPPPS Teaching Model in Advanced Mathematics Teaching

Abstract

This study explores the innovative application of the AI-driven BOPPPS teaching model in advanced mathematics education, aiming to reform traditional teaching processes through intelligent technologies. The model integrates artificial intelligence with the six core elements of BOPPPS: AI-generated situational cases are used to arouse students' interest; personalized learning goals are intelligently formulated
based on students' cognitive levels; diagnostic tests are conducted to identify knowledge gaps accurately; intelligent interactions such as realtime Q&A and group collaboration support are integrated to enhance engagement; a three-round iterative mechanism of "teaching-post-testintelligent feedback-error reinforcement" is constructed to form a closed-loop learning system. Practice shows that this model significantly
improves students' classroom participation, strengthens their knowledge retention, and enhances their mathematical reasoning abilities. By establishing an intelligent "diagnosis-training-remediation" loop, it not only addresses the limitations of traditional teaching methods in meeting
diversified and personalized needs but also provides a replicable technical solution for the reform of advanced mathematics courses, promoting the transformation of teaching from experience-driven to data-driven.