Preservice Teachers’ Prompting Practices and Decision-Making in Using Generative Artificial Intelligence

• In Event: Integrating AI in Pre-service Teacher Education

Abstract

This case study examined six U.S. preservice teachers’ generative artificial intelligence (GenAI) practices during a teacher education intervention. Drawing from chat histories, weekly reflections, and interviews, we explored preservice teachers’ prompting strategies and engagement with ChatGPT in supporting weekly learning tasks, along with their prompting decisions. Findings revealed that explicit, exploratory, and social prompting strategies were most commonly used. In contrast, limited use of adaptive, reflective, and logical strategies was associated with lower engagement and often resulted in minimal prompt iterations. Participants’ decisions in using ChatGPT were largely based on its perceived usefulness in simplifying or enhancing tasks. These findings highlight the need for targeted support to develop higher-order prompting strategies that promote deeper, more intentional GenAI use.

Authors

• Yin Hong Cheah, University of Idaho

• Hsiao-Ping Hsu, Dublin City University

• Jingru Lu, University of Idaho

• Raghad Faisal Alsaka, University of Idaho