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Showing 1 results for Perceived Fairness
Sara Ashouri,
Volume 28, Issue 1 (4-2025)
Abstract
Abstract
The increasing popularity of AI writing tools raises the question of how students perceive the fairness of automated feedback, particularly in comparison with teacher feedback. The perceived fairness of the feedback is an underexplored subject. This paper investigated the perceived fairness of AI writing feedback among English as a Foreign Language (EFL) students compared to teacher writing feedback and the relationship between students’ acceptance of AI writing feedback. The design was quantitative, within-subjects and it was based on 35 B1–B2 EFL students who were enrolled in an English language institute. The participants were given a brief writing assignment and were provided with teacher feedback as well as AI feedback. Subsequently, they completed a survey that assessed perceived fairness, perceived usefulness, perceived ease of use and acceptance (intention to use) AI feedback. The data were analyzed using descriptive statistics, t-tests, correlation, and regression. Findings indicated that teacher feedback was perceived as significantly fairer than AI feedback. The AI feedback was positively evaluated. Perceived fairness was related to students’ acceptance of AI feedback. The regression analyses indicated that acceptance was predicted by perceived fairness, usefulness and perceived ease of use jointly, but usefulness emerged as the strongest predictor. The findings imply that AI feedback is most effective when it is used as a complement to teacher feedback in EFL writing instruction.
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