Dual-Role Dynamics in Prompting: Elementary Pre-service Teachers' AI Prompting Strategies for Representational Choices

TL;DR

This study explores how pre-service elementary teachers adapt their AI prompting strategies to generate effective representations for different audiences, revealing role-based differences in their approach and implications for teacher education.

Contribution

It introduces a novel analysis of role-dependent prompting behaviors in pre-service teachers engaging with AI for pedagogical purposes.

Findings

Teachers are more explicit when acting as instructors.

Broader variety of representations are produced when acting as teachers.

Distinct prompting trends emerge based on the role assumed.

Abstract

Pre-service teachers play a unique dual role as they straddle between the roles of students and future teachers. This dual role requires them to adopt both the learner's and the instructor's perspectives while engaging with pedagogical and content knowledge. The current study investigates how pre-service elementary teachers taking a physical science course prompt AI to generate representations that effectively communicate conceptual ideas to two distinct audiences. The context involves participants interacting with AI to generate appropriate representations that explain the concepts of wave velocity to their elementary students (while casting themselves as teachers) and the Ideal Gas Law to their English teachers (while casting themselves as students). Emergent coding of the AI prompts highlight that, when acting as teachers, participants were more explicit in specifying the target audience, predetermining the type of representation, and producing a broader variety of representations compared to when they acted as students. Implications of the observed 'exploratory' and 'prescriptive' prompting trends across the two roles on pre-service teachers' education and their professional development are discussed.