Design and Practice of AI-Empowered Case-Based Teaching in Cell Biology

The development of AI (artificial intelligence) is gradually transforming the teaching methods of fundamental courses in higher education. Taking Cell Biology as an example, this study aims to explore the design and implementation effects of case-based teaching empowered by AI. To address the abstract and complex nature of Cell Biology, a smart teaching system was constructed by integrating four principles: a student-centered approach, a case-driven methodology, an AI-powered engine, and the seamless integration of ideological and political education. This system, supported by platforms like Rain Classroom and Super Star Learning, covers the entire teaching process: pre-class, in-class, and post-class. Before class, personalized preview resources are distributed. During class, AI is used to clarify easily confused concepts, visualize abstract knowledge, incorporate scientific research cases for ideological and political education, and organize student presentations and course explanations. After class, case studies and problem-driven reviews are conducted to reinforce knowledge transfer and practical skills. The teaching practice results demonstrate that this model significantly improves student learning outcomes. The failure rate for the 2023 cohort decreased by 25.65% compared to the 2022 cohort, with a notable increase in the proportion of students achieving medium to high grades. Course satisfaction exceeded 95%. The results indicate that the systematic integration of AI and case-based teaching not only effectively enhances students’ comprehension of complex cell biological mechanisms but also broadens their academic horizons and strengthens their awareness of scientific ethics. Meanwhile, the study also investigated the impact of AI tool stability on teaching and the students’ usability evaluation of different AI tools, proposing a gradual integration strategy to inform teaching practices. This research provides a replicable and scalable systematic pathway and practical paradigm for reforming biological professional courses in the era of digital and intelligent transformation.

CSTR: 32200.14.cjcb.2025.12.0022