Exploring EFL Secondary Students' AI-generated Text Editing While Composition Writing

TL;DR

This study explores how secondary EFL students interact with and edit AI-generated text during writing, revealing complex behaviors and patterns that challenge assumptions of passive AI use and informing instructional strategies.

Contribution

It provides new insights into students' editing behaviors with AI-generated text, identifying diverse patterns and sources of AI edits during the writing process.

Findings

Identified 15 types of AI-generated text edits across seven categories.

Revealed four distinct editing behavior patterns based on planning and drafting.

Uncovered unexpected sources of AI-initiated edits from iOS and Google Docs.

Abstract

Generative Artificial Intelligence is transforming how English as a foreign language students write. Still, little is known about how students manipulate text generated by generative AI during the writing process. This study investigates how EFL secondary school students integrate and modify AI-generated text when completing an expository writing task. The study employed an exploratory mixed-methods design. Screen recordings were collected from 29 Hong Kong secondary school students who attended an AI-assisted writing workshop and recorded their screens while using generative AI to write an article. Content analysis with hierarchical coding and thematic analysis with a multiple case study approach were adopted to analyze the recordings. 15 types of AI-generated text edits across seven categories were identified from the recordings. Notably, AI-initiated edits from iOS and Google Docs emerged as unanticipated sources of AI-generated text. A thematic analysis revealed four patterns of students' editing behaviors based on planning and drafting direction: planning with top-down drafting and revising; top-down drafting and revising without planning; planning with bottom-up drafting and revising; and bottom-up drafting and revising without planning. Network graphs illustrate cases of each pattern, demonstrating that students' interactions with AI-generated text involve more complex cognitive processes than simple text insertion. The findings challenge assumptions about students' passive, simplistic use of generative AI tools and have implications for developing explicit instructional approaches to teaching AI-generated text editing strategies in the AFL writing pedagogy.