Generative AI’s Impact on Pre-service Teachers’ Instructional Design: A Human–Computer Interaction Empirical Study

• In Event: AERA Poster Session Saturday 1:45 pm
  In Poster Session: Generative AI in Teacher Education: Innovations in Preparation, Practice, and Pedagogy

Abstract

This study explores how generative artificial intelligence (GenAI) can enhance the pedagogical competence of pre-service teachers from a human-computer interaction perspective. Using a mixed-method approach, the study recruited 50 physics education graduate students to complete teaching tasks with and without GenAI support. The participants' instructional designs and collaborative conversations with the AI were analyzed. The results showed that GenAI significantly improved teachers' self-efficacy and design performance, particularly in terms of instructional context and content organization, although it also increased cognitive load. Poorly structured task design remains a challenge due to cue barriers and AI illusions. This study recommends combining GenAI applications with AI-TPACK training to cultivate pre-service teachers' abilities to become innovative, AI-savvy instructional designers.

Authors

• Xu Wang, East China Normal University

• Yu Chen, East China Normal University

• Xiaohua Yu