[Construction and teaching practice of an AI- and OBE-integrated smart course Genetic Engineering]

Several critical challenges exist in the graduate course Genetic Engineering at Beijing Institute of Technology, including conceptual confusion in core topics such as CRISPR-Cas9, difficulties in transferring theoretical knowledge to experimental design, fragmentation of teaching resources across multiple platforms, and the lack of process-oriented assessment for complex reasoning and research competence. In response, we propose a new paradigm that deeply integrates artificial intelligence (AI) and outcome-based education (OBE) in course design and implementation. Specifically, we construct a triadic framework of knowledge graph-ability matrix-intelligent learning companion, in which 421 core knowledge points are systematically mapped to the graduate program's ability indicators, forming a dynamic knowledge-ability matrix that supports personalized learning paths and traceable competence development. A research task-oriented AI companion is further designed for the graduate context. By leveraging retrieval-augmented generation (RAG) and a course-specific large language model (LLM) around comprehensive tasks such as the vector design for gene therapy, the system analyzes students' decision-making trajectories and task outputs to enable process-based evaluation of complex thinking and research skills. In addition, a data-driven teaching analytics cockpit aggregates learning behaviors, knowledge mastery, and ability performance in real time, helping instructors accurately identify conceptual bottlenecks and competence gaps and refine instructional interventions. Teaching practice conducted in the Fall 2025 semester suggests, within the context of this study, that students in the experimental class using the platform achieved higher final examination scores than those in two parallel control classes (P<0.05). The platform was also associated with high learning engagement, with a daily activity rate of 87% and an average of 5.3 questions per student per day. Most students reported observed improvements within this study in self-directed learning efficiency and understanding of advanced technologies. Overall, the findings indicate that compared with conventional smart courses primarily focusing on resource integration and content recommendation, this AI- and OBE-integrated course-centered on authentic research tasks and competence attainment-partially addresses challenges related to knowledge transfer and competence evaluation in graduate education, providing a referenceable practical pathway for smart-course reform in life science graduate programs.