@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Sentiment and Stance in EFL Responses to AI-Generated Environmental Content
%A Sophocleous, Andry
%Y Grasso, Francesca
%Y Basile, Valerio
%Y Bosco, Cristina
%Y Ibrohim, Muhammad Okky
%Y Skeppstedt, Maria
%Y Stede, Manfred
%S Proceedings of the 2nd Workshop on Ecology, Environment, and Natural Language Processing
%D 2026
%8 May
%I European Language Resources Association
%C Palma de Mallorca, Spain
%F sophocleous-2026-sentiment
%X 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.
%R 10.63317/42nje3826fwz
%U https://aclanthology.org/2026.nlp4ecology-1.5/
%U https://doi.org/10.63317/42nje3826fwz
%P 53-59
Markdown (Informal)
[Sentiment and Stance in EFL Responses to AI-Generated Environmental Content](https://aclanthology.org/2026.nlp4ecology-1.5/) (Sophocleous, NLP4Ecology 2026)
ACL