Can AI-Assisted Inquiry Enhance Students' Decision-Making Skills in Socio-Scientific Issues? A Three-Group Experimental Study on Climate Change
Abstract
Climate change is a socio-scientific issue: it rests on science but cannot be settled by science, because any serious response forces people to weigh costs, values, and competing interests under uncertainty. Helping students make such decisions well is a central aim of science education, and the arrival of generative artificial intelligence raises a sharp question: does a conversational AI partner deepen students' reasoning, or simply do the thinking for them? This study tested whether AI-assisted inquiry improves secondary students' decision-making about climate change. Using a pretest-posttest design with three groups (AI-assisted inquiry, inquiry without AI, and traditional instruction; 270 students, 90 per group), reasoning was assessed across seven decision-making steps, from defining the problem to monitoring with adaptive management, using a four-level analytic rubric scored through content analysis with high inter-coder agreement. All three groups began at comparable, mostly low levels and all improved, but the gains differed sharply. The AI-assisted group improved most, ahead of inquiry-only and of traditional instruction. Between-group effect sizes on gains were large for AI-assisted versus traditional instruction and moderate-to-large for AI-assisted versus inquiry-only, with the clearest advantages on stakeholder engagement, alternatives, implementation, and monitoring. Within the AI group, the number of times students checked the AI's claims against the sources predicted their gains, and no student was flagged for over-reliance. The findings suggest that AI helps most when it is designed to question rather than to answer, and that the inquiry it is embedded in carries much of the benefit.
Versions
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BibTeX
@misc{gousopoulos2026aiassisted,
title = {{Can AI-Assisted Inquiry Enhance Students' Decision-Making Skills in Socio-Scientific Issues? A Three-Group Experimental Study on Climate Change}},
author = {Dimitrios Gousopoulos},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.28617},
eprint = {2608.28617},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.28617},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Can AI-Assisted Inquiry Enhance Students' Decision-Making Skills in Socio-Scientific Issues? A Three-Group Experimental Study on Climate Change
AU - Dimitrios Gousopoulos
PY - 2026
DA - 2026/08/01
PB - arXiv
DO - 10.48550/arxiv.2608.28617
UR - https://arxiv.org/abs/2608.28617
AB - Climate change is a socio-scientific issue: it rests on science but cannot be settled by science, because any serious response forces people to weigh costs, values, and competing interests under uncertainty. Helping students make such decisions well is a central aim of science education, and the arrival of generative artificial intelligence raises a sharp question: does a conversational AI partner deepen students' reasoning, or simply do the thinking for them? This study tested whether AI-assisted inquiry improves secondary students' decision-making about climate change. Using a pretest-posttest design with three groups (AI-assisted inquiry, inquiry without AI, and traditional instruction; 270 students, 90 per group), reasoning was assessed across seven decision-making steps, from defining the problem to monitoring with adaptive management, using a four-level analytic rubric scored through content analysis with high inter-coder agreement. All three groups began at comparable, mostly low levels and all improved, but the gains differed sharply. The AI-assisted group improved most, ahead of inquiry-only and of traditional instruction. Between-group effect sizes on gains were large for AI-assisted versus traditional instruction and moderate-to-large for AI-assisted versus inquiry-only, with the clearest advantages on stakeholder engagement, alternatives, implementation, and monitoring. Within the AI group, the number of times students checked the AI's claims against the sources predicted their gains, and no student was flagged for over-reliance. The findings suggest that AI helps most when it is designed to question rather than to answer, and that the inquiry it is embedded in carries much of the benefit.
ER -