Research on the Mode Innovation and Practical Efficacy of AI Teaching Assistants Empowering Higher Vocational Classrooms

Abstract

 The digital intelligence era has driven a fundamental shift in vocational education paradigms, moving from “tool application” to “ecosystem reconstruction.” To address the challenges of AI integration in vocational classrooms—characterized by “difficult implantation and superficial fusion”—this study proposes and validates the “PAC” teaching model, which facilitates tripartite collaboration among teachers, AI, and students. The study elaborates on the upgraded role of the AI teaching assistant embedded throughout the instructional process. In the Preparation phase, the assistant serves as a personalized navigator for targeted preview. During the Acting phase, it transforms into an intelligent collaborator to deepen skill acquisition. In the Consolidation phase, it acts as a precision evaluator to close the learning loop. Findings indicate that this model can enhance students’ ability to transfer learning to complex tasks by an average of 42.6%, reduce teachers’ instructional planning time by nearly 30%, and cultivate a new classroom ecology characterized by “data-driven instruction, personalization, and intelligent symbiosis.” This study offers an embodied deconstruction and empirical support for integrating AI into vocational education.