TRV-2026-1464Certified recordPeer-reviewed

The AI Mirror: A Qualitative Study of Student Autonomy and Dependence in ESL Writing

This article explores the role of Artificial Intelligence (AI) in English as a Second Language (ESL) writing, examining whether it fosters autonomy or promotes dependency. Utilizing a qualitative methodology, semi-structured interviews were conducted following a timed writing task with 40 intermediate-level ESL students across three higher education institutions in Oman. Thematic analysis reveals that AI's impact on student autonomy is highly contingent upon its pedagogical application. When utilized strategical…

Education · The Trace — both readings · certified 2026-10-15 · v1 · article view · machine-readable

Current reading — gain

When ESL students use AI strategically for brainstorming and personalized feedback, it works as a scaffold that supports self-directed learning.

Current reading — problem

When ESL students lean on AI to generate whole texts or passively fix errors, they offload thinking and become less independent writers.

What this doesn’t fix

The evidence comes from interviews with 40 intermediate-level ESL students at three institutions in Oman, so it reflects self-reported experiences in a limited population rather than measured outcomes across wider groups.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-1464, v1: “The AI Mirror: A Qualitative Study of Student Autonomy and Dependence in ESL Writing.” Truvace, 2026-10-15. /record/TRV-2026-1464 (accessed at citation time). sha256 6207f6a8c9fc9aa0…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv16207f6a8c9fc…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-1464 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.