Year: 2026
Type: conference-paper
Abstract: 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 suggest 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.
Author: Andry Sophocleous
Institution: University of Nicosia
Language: English
Subfield: Literature and Literary Theory
Field: Arts and Humanities
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7166546246