Exploring how EFL students talk to and through AI to develop texts

TL;DR

This study investigates how EFL students interact with AI chatbots through prompt engineering and authorship negotiation, analyzing their strategies and patterns to understand implications for writing pedagogy.

Contribution

It identifies distinct student profiles in human-AI interaction and examines their relation to writing performance, offering insights for EFL teaching methods involving AI.

Findings

Ten prompting strategies identified, including questions and instructions.

Three student profiles in AI interaction: AI-dominant, Human-dominant, Collaborative.

No significant effect of interaction style on writing quality.

Abstract

Generative Artificial Intelligence (AI) introduces new considerations for English as a foreign language (EFL) writing pedagogy. This study explores how students talk to and through AI by prompt engineering and negotiating authorship, respectively, and whether any patterns in the latter relate to students' writing performance. Using an exploratory mixed methods design, we analyzed screen recordings of 44 Hong Kong secondary students completing a Curricular Writing Task with AI Chatbots. Content analysis identified ten types of prompting strategies students employed, including questions, searches, and detailed instructions. From clustering these strategies, three distinct profiles of human-AI rhetorical load responsibility emerged: AI-dominant (52% of students), Human-dominant (25%) and Collaborative human-AI (14%). A MANOVA analysis indicated no significant multivariate effect of rhetorical load responsibility on three dimensions of students' writing performance: content, language, and organization. Students' prompting strategies and rhetorical load responsibility patterns have implications for their engagement and autonomy in EFL writing pedagogy.