Research on the Impact of Human-Machine Collaborative Dialogue on Normal University Students' Reflection and Instructional Design Abilities in Teaching Resources

Abstract

In the era of AIGC, the application of human-machine collaborative dialogue, grounded in GAI, holds significant promise within the educational and instructional domains. The integration of artificial intelligence with normal education has emerged as a pivotal topic in the cultivation of prospective teachers. The study has employed exploratory experiments with single pre-post measurements and epistemic network analysis to investigate the influence of human-machine collaborative dialogue on the dialectical reflection and instructional resource design capacities of normal students. The research reveals the following insights: Human-machine collaborative learning activities based on AIGC can enhance the dialectical reflection ability of normal education students, and different human-machine collaborative behaviors have varying degrees of impact; The activities can effectively improve the instructional resource design capacity of normal education students, and different human-machine collaborative behaviors have varying degrees of impact; The human-machine collaborative dialog based on AIGC can indirectly affect students’ instructional resource design capacity by changing their dialectical reflection ability. Drawing on these findings, the research offers recommendations on leveraging generative artificial intelligence to more effectively nurture the reflective and instructional resource design competencies of normal students.