Based on the ICAP Framework: An Empirical Study of Generative Artificial Intelligence's Enhancement of Preservice Teachers' Instructional Design Ability

Abstract

Generative Artificial Intelligence (AI) has seen increasing application in the field of education, particularly in enhancing pre-service teachers' instructional design abilities, demonstrating significant potential. Drawing on the ICAP framework theory, this paper conducts an empirical study using the generative AI tool Wenxin Yiyan. This study involved 32 pre-service teachers majoring in Educational Technology, each submitting an instructional design for basic IT instruction in elementary schools, both before and after the application of generative AI. After the experiment, a questionnaire survey was conducted to obtain feedback from pre-service teachers on their use of generative AI. The collected data were analyzed using paired T-tests and cognitive network analysis. The results showed that generative AI assistance significantly improved the quality of teaching design plans and enhanced the teaching design capabilities of pre-service teachers. Moreover, pre-service teachers of different performance levels demonstrated distinct cognitive network characteristics when using generative AI assistance.