Climate-control ChatGPT for EFL writing and AI literacy

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Original abstract

ChatGPT is increasingly used in EFL writing, yet applied linguistics lacks robust methods for measuring whether human-AI dialogue improves learners’ AI literacy, harm-sensitive reasoning, and independent argumentative writing. This mixed-methods quasi-experimental study examines whether climate-control ChatGPT, a prompted interactional design that requires learners to justify, qualify, counterexample, and re-author their own claims, produces stronger gains than standard ChatGPT use and no-ChatGPT instruction. The study involved 120 undergraduate EFL learners in six intact second-year academic writing sections at a public university in Jordan. Participants were assigned to no-ChatGPT control, standard ChatGPT, or climate-control ChatGPT conditions. Data included no-AI diagnostic, pretest, posttest, and delayed transfer essays; ChatGPT dialogue logs; first and final drafts; learner reflections; uptake traces; stimulated-recall interviews; and 9,113 HARM-CAL annotated language units. HARM-CAL, Harm-sensitive Reasoning and Anthropomorphism Calibration in Language, classified learner language for naïve relativism, moral imposition, harm-sensitive reasoning, AI anthropomorphism, calibrated AI literacy, and reflective uncertainty. Results showed that the climate-control condition produced the largest gains in harm-sensitive AI literacy and argumentative writing quality, with advantages maintained on delayed no-AI transfer. It also reduced direct uptake, increased self-repair and authorial control, and strengthened calibrated distinctions between AI output competence and consciousness. The study contributes a classroom-tested intervention and a transparent computational framework that combines reproducible annotation procedures, a fully specified TF-IDF baseline, and archived transformer-family classifier outputs for analyzing responsible AI literacy in second-language argumentation.

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