AIGC赋能下应用型高校“人机协同双师课堂”教学模式重塑及应用

The Reconstruction and Practical Design of a Human-AI Collaborative Dual-Teacher Classroom Model in Application-Oriented Universities Empowered by AIGC

摘要: 生成式人工智能(AIGC)发展迅速，正引领高校课堂从信息化到智能化、个性化和协同化转变。应用型高校致力于培养高素质应用型人才，传统的统一进度、统一内容和统一评价课堂教学方式不能很好地适应学生的基础不同、掌握技能的速度不同以及提升实践能力的需求。站在“技术–教育”的角度，本文在总结分析AIGC助力高校教育教学研究成果基础上，并考虑到应用型高校的专业课程具有较强的实践性、任务性和情境性的特征，“人类教师–AIGC虚拟双师–学生”三者构成的“人机协同双师课堂”教学模式应运而生。这种模式以教师为主导，AI为助手，学生为主体，评价反哺的原则进行，规定了人教的引导方向是价值观引导、任务布置、高阶思维启发以及实践指导等工作，AI的工作则是对学生的学习情况进行分析、提供学习资源、及时解答疑问、分层次指导和支持以及对整个过程进行总结等。根据应用型人才的需求，形成了“课前智能化测评–课中协助教学–课后个性化辅导–过程性评价反哺”的流程并且提出了基于知识库、检索增强生成、过程性评价和伦理治理的方法。本文主要有两个突破点：一是从“AI工具辅助教学”到“人机协同双师课堂”的转变；二是从结果性考试到“诊断–反馈–干预–优化”的循环。从一般的AIGC应用到应用于应用型高校专业的课程场景化教学设计。可以为应用型高校推进大规模个性化学习、精准教学以及课堂教学治理创新提供借鉴。

Abstract: The fast growth of Artificial Intelligence Generated Content (AIGC) has encouraged the conversion of university classrooms as digital aids into intelligent, individualized, and interactive learning settings. Application-focused universities seek to produce high-quality applied talents; nevertheless, traditional teaching methods with the same rate of learning, content distribution, and evaluation cannot always meet the needs of students in terms of their prior knowledge, speed of skill acquisition, and practical learning requirements. Drawing on the conceptual framework of mutual creation of technology and education, this paper will present a human-AI cooperation dual-teacher classroom model with a ternary interaction relationship of human teacher-AIGC virtual teacher-student. The model follows the concepts of teacher leadership, AI cooperation, student engagement, and evaluation feedback. The human teacher takes care of value guidance, task design, higher-order thinking facilitation and practical instruction, and AIGC is used as an intelligent assistant for learning diagnosis, resource generation, real-time tutoring, differentiated support, and process analysis. Closed loop teaching system: pre-class intelligence diagnosis-in-class collaboration guidance-post-class specific remedial measures-process feedback evaluation. This paper also offers implementation strategies that will be founded on professional knowledge bases, retrieval-augmented generation, process-oriented evaluation, and ethical governance. The contributions of this work include the conversion of AI as a supplemental tool into a dual-teacher classroom structure, the transition of assessment as an outcome-focused examination to a reflexive process of diagnosis, feedback, intervention, and optimization, and the creation of scenario-based teaching designs to suit application-oriented universities. The present study will serve as a guide to implement large-scale personalized learning, precise teaching, and innovation in classroom management in applications-oriented higher education.