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Sentiment and Stance in EFL Responses to AI-Generated Environmental Content

Recent advances in generative AI have enabled the large-scale production of environmental imagery and descriptions, yet questions remain regarding how such content represents emotion, agency, and responsibility. This study examines how human evaluators respond to AI-generated environmental representations, focusing on sentiment, stance, and argumentation as dimensions of qualitative evaluation. Data were collected from 81 multilingual secondary-school EFL learners in Cyprus, who engaged with AI-generated environmental images and accompanying AI-written descriptions through a sequence of structured tasks. Using qualitative discourse analysis informed by sentiment- and stance-oriented frameworks, the study analyses learner-produced texts to identify affective evaluations, moral positioning, and alignment with or challenge to AI-generated discourse. Findings indicate that participants consistently moved beyond surface-level description to articulate emotional engagement, assign responsibility, and critique omissions in AI-generated content, particularly regarding the representation of human-environment relations. The study contributes to research on human-centered AI evaluation by demonstrating the value of sentiment and stance analysis for assessing AI-generated environmental language, and highlights the potential of educational contexts as sites for examining human interpretive responses to automated discourse.