Abstract: Organic chemistry laboratory is a fundamental, compulsory course in chemistry and related disciplines, with synthesis experiments at their core. This paper introduces artificial intelligence prediction models into the teaching of synthesis experiments in organic chemistry courses. Specifically, the ASKCOS model is applied to perform both forward synthesis prediction and retrosynthesis prediction. The results indicate that the existing model provides reliable predictions for the synthesis experiments covered in the curriculum, with hit rates for both forward and retrosynthesis predictions reaching as high as 88.5%. The integration of AI prediction models into undergraduate laboratory teaching enhances students’ understanding of organic chemical synthesis, promotes the concept of AI-assisted organic synthesis, fosters interdisciplinary collaboration, improves teaching quality, and encourages divergent thinking, thereby laying a strong foundation for their future research work.

Key words: AI Model, Organic synthesis, Forward synthesis prediction, Retrosynthesis prediction