AI detection and grading fairness in EFL assessment: a qualitative study of student and teacher perceptions at a University in Uzbekistan

This study examines how undergraduate EFL students and teachers at Fergana State University in Uzbekistan perceive AI detection and grading fairness in writing assessments. Using a qualitative exploratory design, structured interviews were conducted with fourteen undergraduate students and eight EFL teachers. Data were analysed using reflexive thematic analysis. Student data generated four themes: perceived unfairness in AI-related grading, strong writing as a trigger for suspicion, the emotional and behavioural impact of suspected AI use, and the absence of clear institutional rules. Teacher data produced four parallel themes: reliance on informal heuristics in the absence of detection tools, inconsistent grading responses to suspected AI use, awareness of bias and the risk of misjudging strong students, and frustration with the lack of institutional guidance. The absence of clear policy appeared to create conditions in which both students and teachers perceived grading practices as inconsistent or unfair. The study contributes evidence from an underrepresented Central Asian EFL context, where both students and teachers expressed a strong desire for clearer institutional guidance on AI use in assessment, suggesting that locally developed policies may help address the uncertainty both groups described.

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