Human–AI Feedback in EFL Academic Writing: Perceptions and Complementarity in Ecuadorian Education
Keywords:
Human–AI feedback; academic writing; English as a foreign language; generative artificial intelligence; secondary education.Abstract
Generative artificial intelligence has expanded the sources of feedback available to secondary school students who write in English as a foreign language, but its educational value depends on how automated responses are articulated with teacher judgement rather than on speed alone. This study examined the perceived roles, strengths, and complementarity of teacher and AI-generated feedback in Ecuadorian secondary education. A quantitative, cross-sectional, non-experimental, descriptive-comparative design was used with the original dataset of 60 secondary school students and 5 English-language teachers. Students completed a 48-item questionnaire and teachers a 54-item questionnaire, both using five-point Likert scales. Descriptive statistics, confidence intervals, agreement percentages, paired comparisons, and exploratory non-parametric tests were applied. Students evaluated teacher feedback (M = 4.04) and AI-generated feedback (M = 3.98) favourably, without a statistically significant global difference, t (79) = 1.40, p = .164. AI was mainly associated with objectivity, accessibility, and immediacy, whereas teachers were valued for personalization, understanding learner needs, and explanation quality. Teachers also reported favourable perceptions of institutional readiness (M = 4.07), behavioural intention (M = 4.03), and AI effectiveness (M = 3.99), alongside moderate ethical concern (M = 3.02). Because several item blocks showed weak internal-consistency diagnostics, findings are interpreted primarily at the item level and as exploratory evidence. The results favour a complementary human–AI feedback model while underscoring the need for validated instruments and direct measures of writing performance.
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