@inproceedings{sophocleous-2026-sentiment,
    title = "Sentiment and Stance in {EFL} Responses to {AI}-Generated Environmental Content",
    author = "Sophocleous, Andry",
    editor = "Grasso, Francesca  and
      Basile, Valerio  and
      Bosco, Cristina  and
      Ibrohim, Muhammad Okky  and
      Skeppstedt, Maria  and
      Stede, Manfred",
    booktitle = "Proceedings of the 2nd Workshop on Ecology, Environment, and Natural Language Processing",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.nlp4ecology-1.5/",
    doi = "10.63317/42nje3826fwz",
    pages = "53--59",
    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 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."
}