Product vs. Process: Exploring EFL Students' Editing of AI-Generated Text for Expository Writing

TL;DR

This study investigates how EFL students edit AI-generated texts during expository writing, revealing editing patterns and the limited impact of editing on improving composition quality, emphasizing the need for genre-specific instruction and process-focused assessment.

Contribution

It provides new insights into students' editing behaviors with AI-generated texts and their limited effect on writing quality, highlighting implications for teaching and assessment strategies.

Findings

Two editing patterns identified: refining introductions and extensive body edits.

AI-generated words positively predict overall writing scores.

Editing effort shows minimal impact on improving composition quality.

Abstract

Text generated by artificial intelligence (AI) chatbots is increasingly used in English as a foreign language (EFL) writing contexts, yet its impact on students' expository writing process and compositions remains understudied. This research examines how EFL secondary students edit AI-generated text. Exploring editing behaviors in their expository writing process and in expository compositions, and their effect on human-rated scores for content, organization, language, and overall quality. Participants were 39 Hong Kong secondary students who wrote an expository composition with AI chatbots in a workshop. A convergent design was employed to analyze their screen recordings and compositions to examine students' editing behaviors and writing qualities. Analytical methods included qualitative coding, descriptive statistics, temporal sequence analysis, human-rated scoring, and multiple linear regression analysis. We analyzed over 260 edits per dataset, and identified two editing patterns: one where students refined introductory units repeatedly before progressing, and another where they quickly shifted to extensive edits in body units (e.g., topic and supporting sentences). MLR analyses revealed that the number of AI-generated words positively predicted all score dimensions, while most editing variables showed minimal impact. These results suggest a disconnect between students' significant editing effort and improved composition quality, indicating AI supports but does not replace writing skills. The findings highlight the importance of genre-specific instruction and process-focused writing before AI integration. Educators should also develop assessments valuing both process and product to encourage critical engagement with AI text.