人工智能赋能“微生物学实验”闭环教学模式的构建

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

摘要:

人工智能(artificial intelligence, AI)的迅猛发展为教育教学范式变革注入新动能。针对“微生物学实验”教学中存在的内容固化、资源受限、时空约束及评价单一等问题，本研究构建了一种AI赋能的闭环教学模式。该模式将AI技术全面融入教学全流程：课前，通过AI技术构建微生物学实验课程知识库，集成AI助教，实施动态学情分析与个性化资源推送，平台支持教师智能化备课与学生自适应预习，实现教学资源整合与个性化路径生成；课中，运用AI驱动的虚拟现实(virtual reality, VR)和增强现实(augmented reality, AR)实验室，支持学生进行无风险、可重复的自主实验操作，进行安全操作演练并获取实时纠错反馈；课后，依托AI生成学生个性化学习画像，构建智能化测评体系，实现智能评估与多维评价。由此形成数据驱动、虚拟实操与精准反馈相融合的AI闭环教学系统，有望克服传统教学在设备依赖、安全风险及个性化指导方面的局限，为微生物学实验教学智能化转型提供实践范式与理论参考。

Abstract:

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.