Research on the Application of Generative AI in Vocational Education Practice Teaching

Abstract

With the rapid development of artificial intelligence technology, its potential in the field of vocational education has attracted widespread attention from both the academic and educational communities. This study is based on the current state of practical teaching in vocational education and delves into the potential applications of Generative AI in terms of teaching model innovation, generation of educational resources, and enhancement of learning outcomes. The paper employs an empirical research method, constructing an interactive teaching experimental platform, and inviting 300 vocational school students and 30 professional teachers to participate in practical teaching activities to evaluate the effect of Generative AI teaching systems in improving students' vocational skills and theoretical knowledge. The results show that the application of Generative AI technology significantly increases student motivation and participation, and the experimental group outperforms the control group in both hands-on skills and theoretical examinations. In terms of teacher workload, the generation of teaching content and simulated scenarios with the aid of Generative AI saved a considerable amount of preparation time, allowing teachers to focus more on classroom interaction and personalized instruction. Furthermore, the study proposes a series of innovative strategies for the application of Generative AI in education, including the construction of personalized learning roadmaps, the automatic generation of simulated training scenarios, and the design of intelligent assessment and feedback systems. The implementation of these strategies not only promotes the improvement of vocational education quality but also lays the groundwork for deeper development of educational informatization and intelligence. The research also offers specific recommendations for the widespread application of Generative AI in vocational education and looks ahead to possible challenges and future research directions.