[Construction of an artificial intelligence-enabled closed-loop teaching model for Microbiology Experiments]

The rapid advancement of artificial intelligence (AI) has created new opportunities for the transformation of educational paradigms. To address challenges in microbiology laboratory teaching, including content rigidity, resource constraints, spatiotemporal limitations, and uniform assessment, this study developed an AI-enabled closed-loop teaching model. This model integrates AI technologies throughout the entire teaching process. Before class, a microbiology laboratory course knowledge repository was constructed through AI technology, an AI teaching assistant was integrated, and dynamic learning analytics and personalized resource recommendations were implemented. The platform supports intelligent lesson preparation for teachers and adaptive pre-class learning for students, thereby enabling teaching resource integration and personalized pathway generation. During class, AI-powered virtual reality (VR) and augmented reality (AR) laboratories were utilized to allow students to perform risk-free and repeatable experiments independently, thereby enabling safe operational training and real-time error-correction feedback. After class, AI generated personalized learning profiles and established an intelligent evaluation framework, enabling intelligent assessment and a multi-dimensional evaluation system. Thus, a data-driven, AI-enabled closed-loop teaching system emphasizing virtual practice and precise feedback was formed. This model is expected to overcome the limitations of conventional teaching methods, such as heavy reliance on equipment, safety concerns, and a lack of individualized guidance, thereby offering theoretical support and a practical paradigm for the intelligent transformation of microbiology laboratory teaching.