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How does AI match students’ proficiency levels to provide support? Student-driven AI-mediated scaffolding in EFL writing instruction
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DOI：10.1016/j.system.2026.104119.png)
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Abstract
En 中文
The emergence of artificial intelligence (AI) has brought innovative changes to language instruction by offering personalized and flexible educational opportunities. Although AI is widely recognized for delivering personalized feedback, its potential as a systematic, student-driven, mediated scaffolding tool that calibrates support to students’ evolving writing needs remains underexplored. We therefore designed a student-driven AI-mediated scaffolding model (AI-MSM) for writing instruction and examined whether it was associated with gains in English as a foreign language (EFL) students’ writing performance, engagement, and motivation. Forty Chinese EFL students were assigned to the experimental group and participated in a 10-session AI-MSM intervention, wherein they received implicit and explicit AI-mediated scaffolding to support their writing processes. Another 40 students received traditional writing instruction and served as the control group. Empirical data were collected through pre- and post-writing tests, engagement scales, motivation scales, and semi-structured interviews. The results of MANCOVA demonstrated that students in the AI-MSM condition showed greater gains in writing performance and engagement than those in the control group and exhibited preliminary trends in certain motivational dimensions under the present instructional arrangement. These findings suggest that AI-MSM may hold potential for addressing persistent challenges, such as insufficient personalized feedback in EFL writing instruction, although its implications for motivation warrant further investigation. To further explore students’ perspectives on AI-mediated scaffolding, we conducted semi-structured interviews, which revealed students’ positive perceptions of AI’s potential as a mediated scaffolding tool in EFL writing instruction. The study provides a theoretical framework and practical guidelines for optimizing AI-assisted writing instruction.
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