Abstract:

Objective Artificial intelligence (AI) provides a more convenient approach, more accurate strategies, and more timely feedback evaluation for self-learning. This study aims to explore practice and effectiveness evaluation of AI driven after-class exercises in systematic anatomy retake teaching. Methods Adopting a historical controlled study design and taking the nervous system of the course of Systemic Anatomy as an example, using AI large models (DeepSeek and Kimi) to construct after-class exercises and to analyze the effectiveness of AI in empowering self-learning. Results The results showed that 97.83% of students believe that after-class exercises are helpful for understanding and memorizing knowledge, and 89.13% of students believe that after-class practices are helpful in improving learning initiative. Furthermore, using AI models to generate after-class exercises significantly improved students' test scores. The score (9.97±6.99) of the after-class exercises group was significantly more than that (6.66±5.78) of the control group (P <0.01). Conclusion In conclusion, this study suggests that AI driven after-school exercises can help improve students' willingness to learn independently and academic performance.

Key words:  , Artificial intelligence｜ Systematic Anatomy｜Self-learning｜ After-class exercise｜ Educational philosophy｜Teaching reform