Frontiers in psychology · 2026
College students' metacognitive awareness of generative-AI reliance: an experimental study of decision confidence and attribution bias.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ren Shaowei
Аннотация
INTRODUCTION: Generative AI is increasingly used by college students for explanation, evaluation, and decision support, raising the question of whether perceived reliance accurately tracks the extent to which AI advice shapes final judgments.
METHODS: In a three-condition between-subjects experiment, 342 undergraduate students completed reasoning and information-evaluation tasks under independent decision-making, open multi-turn ChatGPT support, or the same ChatGPT support with a brief metacognitive reflection prompt. Participants reported initial and final answers and confidence, and AI-assisted chat logs were coded for the final recommendation, its correctness, and behavioral reliance.
RESULTS: Open ChatGPT support increased final confidence and was associated with 62.4% acceptance of incorrect AI advice. Reflection reduced incorrect-advice acceptance to 39.7% (OR = 0.40, 95% CI [0.28, 0.56], p < 0.001), improved awareness calibration (0.59 vs. 0.41, p < 0.001), and reduced the attribution-bias index (0.21 vs. 0.42, p = 0.002). Recommendation accuracy was comparable between the two AI-assisted groups.
DISCUSSION: Brief metacognitive reflection improved immediate discrimination between useful and misleading AI advice without producing blanket rejection of AI. The exploratory association between calibration and overreliance is not interpreted as causal mediation, and the findings concern bounded academic decision tasks rather than long-term learning.
METHODS: In a three-condition between-subjects experiment, 342 undergraduate students completed reasoning and information-evaluation tasks under independent decision-making, open multi-turn ChatGPT support, or the same ChatGPT support with a brief metacognitive reflection prompt. Participants reported initial and final answers and confidence, and AI-assisted chat logs were coded for the final recommendation, its correctness, and behavioral reliance.
RESULTS: Open ChatGPT support increased final confidence and was associated with 62.4% acceptance of incorrect AI advice. Reflection reduced incorrect-advice acceptance to 39.7% (OR = 0.40, 95% CI [0.28, 0.56], p < 0.001), improved awareness calibration (0.59 vs. 0.41, p < 0.001), and reduced the attribution-bias index (0.21 vs. 0.42, p = 0.002). Recommendation accuracy was comparable between the two AI-assisted groups.
DISCUSSION: Brief metacognitive reflection improved immediate discrimination between useful and misleading AI advice without producing blanket rejection of AI. The exploratory association between calibration and overreliance is not interpreted as causal mediation, and the findings concern bounded academic decision tasks rather than long-term learning.