Abstract: The rapid advancement of artificial intelligence technology has positioned it as a pivotal driver in transforming chemical education. This study presents an infrared spectroscopy intelligent recognition and teaching assistance system developed for n-butyl halide synthesis experiments, based on Nankai University’s AI innovation platform (NK-GeniOS). The system employs monotonicity anti-aliasing optimization to achieve rapid and accurate identification of infrared absorption peaks from spectral images for both products and impurities. Through a multi-agent collaborative framework comprising analysis Agent and evaluation Agent, the system provides precise experimental data interpretation and personalized feedback, addressing the limitations of traditional teaching methods that heavily rely on instructor experience and lack real-time guidance. Implemented via project-based learning groups following a “research-presentation-discussion” model, this approach enables tiered learning outcomes for students at different levels. The system significantly enhanced student engagement, with 79% of participants expressing willingness to participate in similar research projects, while flipped classroom participation rates increased dramatically from 20% to 71%.

Key words: Infrared spectroscopy, Artificial intelligence, Organic chemistry experiment, Interdisciplinary integration