Opaque AI rules leave Iranian students torn over their writing
Interviews suggest that students can feel both relief and shame when using AI in high-stakes English writing assessments.
Editorial illustration — not from the study.
Researchers interviewed Iranian undergraduate students at a public university about experiences using generative AI during high-stakes academic writing assessments. The interviews, conducted in Persian, focused on how students made decisions about AI use and how they felt before and after submitting their work.
The analysis identified three broad patterns: students faced conflicting demands from the assessment system; some experienced a disrupted sense of authorship alongside both relief and shame; and they developed their own ways of judging what was acceptable because institutions offered little guidance. The researchers propose transparency, integration and stronger relationships between teachers and students as principles for designing assessment in settings where AI is available.
How students experienced AI
The interviews pointed to a structural conflict: students were expected to submit highly polished writing that appeared to be produced independently, while AI tools offered help with the very task being assessed. This left participants navigating rules and incentives that pulled in opposite directions.
Students also described a changed relationship with their writing. AI could ease anxiety about writing in English, but using it could reduce their sense of authorship and bring feelings of shame, guilt or inauthenticity. Keeping AI use secret could be isolating. In the absence of clear institutional guidance, students had to work out their own boundaries around what counted as acceptable use.
The researchers interpret these experiences as undermining students’ feelings of autonomy, competence and connection with others. From these findings, they propose three principles for human-centered assessment: transparency about expectations, integration of AI into assessment rather than treating it only as a prohibited tool, and relationality, meaning attention to supportive relationships in the assessment process.
Why clear rules matter
The study shifts attention from whether AI can be detected to how assessment rules are experienced by the students expected to follow them. Its findings suggest that unclear or conflicting expectations can make writing assessment feel less like a measure of students’ abilities and more like a private moral negotiation.
The proposed principles offer a way to address accountability while also considering students’ well-being. They do not establish that these changes would improve learning or trust, but they identify transparency, purposeful treatment of AI and supportive relationships as issues for assessment designers to consider.
Evidence and caveats
The evidence comes from an Interpretive Phenomenological Analysis of single, 60- to 90-minute interviews with 11 purposively selected undergraduate students at one Iranian public university. Interviews were conducted in Persian, transcribed, and translated into English; a bilingual qualitative researcher independently reviewed about 20% of the translated material.
This is a detailed, context-specific account rather than a statistically generalizable study. It covers one institution and one point in time, and participants’ retrospective self-reports may not match their actual writing behavior. The study did not directly observe writing processes or analyze submitted texts. The sample also did not systematically represent differences in English proficiency, socioeconomic background or prior academic achievement, and the findings may not transfer to other universities, countries or student groups.
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Language Testing in Asia · 2026 · DOI: 10.1186/s40468-026-00475-z
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