Digital Intelligence-Driven Innovation: Restructuring the Teaching Mode of Comprehensive Design Experiments in Chemical Biology

在数智化时代背景下，生成式AI为化学生物学综合实验教学带来革新性突破。针对传统验证性实验存在的创新束缚、跨学科整合不足及安全风险限制等问题，提出基于生成式AI的智能教学体系重构方案。通过Transformer等AI深度学习模型实现复杂分子结构生成，构建虚实联动的虚拟仿真实验空间和智能资源推送机制。典型教学案例显示，AI辅助的分子设计工作坊和强化学习驱动的合成路径优化模块可显著提升学生创新能力，助力学生科研素养及批判性思维系统性的培养。重塑实验教学模式，为交叉学科教育范式转型提供实践路径。In the context of the digital intelligence era, generative AI has brought revolutionary breakthroughs to comprehensive experimental teaching in chemical biology. Addressing issues such as innovation constraints, insufficient interdisciplinary integration, and safety risk limitations in traditional verification-based experiments, this paper proposes a reconstruction scheme for an intelligent teaching system based on generative AI. Through AI deep learning models like Transformer, complex molecular structure generation is achieved, constructing an interactive virtual-real experimental space with intelligent resource push mechanisms. Typical teaching cases demonstrate that AI-assisted molecular design workshops and reinforcement learning-driven synthetic pathway optimization modules can significantly enhance students’ innovative capabilities, supporting the systematic cultivation of their scientific research literacy and critical thinking. This reconstruction of the experimental teaching model provides a practical path for the transformative evolution of interdisciplinary education paradigms.