Original title: The Teacher-Guided Generative AI for Sustainable EFL Writing Development: Longitudinal Validation of the AI-MWD Framework
The study compares two groups of undergraduate learners writing in English as a foreign language. Both use the same AI tools, but one group also gets a teacher-guided plan for using them. That group shows lower mental overload and greater ability to reflect on and guide its own writing. Both groups improve accuracy at first, but only the teacher-guided group keeps those gains six weeks later.
Imagine learning to cook while someone gives you advice at every step. Too many instructions at once can leave you following directions without learning why they work. In this paper, teacher guidance is the help that is gradually reduced, while the AI's feedback supports each writing task; the aim is for learners to use those strategies on their own.
EFL learners can get instant feedback from generative AI, but too much unstructured feedback may overwhelm them or make them rely on AI revisions. This study suggests that teachers can guide how that support is used, helping learners keep writing gains and build more independence.
The abstract describes a 14-week quasi-experiment with forty undergraduate EFL learners, so it does not establish that the results apply to other learners, settings, or longer periods. It reports lasting accuracy gains, but does not say whether other writing measures also persisted or how the framework compares with other forms of teacher guidance.
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The Teacher-Guided Generative AI for Sustainable EFL Writing Development: Longitudinal Validation of the AI-MWD Framework — Tugsuu Jargal, Sainzaya Sumiya — Embedded Selforganising Systems — doi:10.14464/ess.v13i16.986
Abstract licence: CC BY
Explanation generated by Synthesis from the abstract — it is not the original paper.