Exploring Cognitive Engagement in a Guided ChatGPT-Supported Collaborative Lesson Plan Refinement for Preservice Teachers (Poster 31)

• In Event: AERA Poster Session Friday 8:00 a.m.
  In Poster Session: Feedback, Teacher Support, and Instructional Approaches

Abstract

This study explores the cognitive engagement of preservice teachers in lesson plan refinement using guided ChatGPT support. Twelve participants revised lesson plans collaboratively with ChatGPT, utilizing structured prompt templates designed for lesson plan analysis and lesson planning. The study employed qualitative data analysis, including transcription and coding of group discussions, and Epistemic Network Analysis (ENA) to assess cognitive engagement in the lesson plan refinement process. Results indicate dense and intricate cognitive engagement, with high-performance groups showing complex interaction networks and deeper reflection. Different prompt templates triggered distinct cognitive processes, highlighting their complementary roles. The findings emphasize the importance of providing structured guidance to maximize the effectiveness of AI tools in supporting preservice teachers’ lesson planning and refinement.

Authors

• Yanyan Sun, East China Normal University

• Yingfen Huang, East China Normal University