ArticleFrontiers in artificial intelligence2026
Climate-control ChatGPT for EFL writing and AI literacy.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.