{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T06:05:15Z","timestamp":1790057115571,"version":"4.0.1"},"reference-count":71,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:p>\n                    Generative artificial intelligence (GenAI) has rapidly become an everyday learning resource for university students, yet evidence on whether such use is associated with stronger or weaker critical thinking is sharply divided. Recent work documents both cognitive scaffolding and \u201cmetacognitive laziness\u201d effects, suggesting that the AI-cognition relationship is contingent on how students engage with AI-generated feedback. Drawing on the extended-mind framework, cognitive load theory and feedback-literacy theory, we test a cross-sectional moderated mediation model in which GenAI feedback literacy mediates the association between GenAI use and critical thinking, operationalised as self-reported critical analytical skills, and reflective thinking moderates both the direct path and the path from feedback literacy to critical thinking. A sample of 421 Chinese undergraduates (69.6% female) completed validated self-report measures, and partial least squares structural equation modelling (PLS-SEM, 5,000 bootstrap resamples) was used to estimate the model. GenAI use significantly predicted critical thinking (\n                    <jats:italic>\u03b2<\/jats:italic>\n                    \u202f=\u202f0.257,\n                    <jats:italic>p<\/jats:italic>\n                    \u202f&amp;lt;\u202f0.001) and feedback literacy (\n                    <jats:italic>\u03b2<\/jats:italic>\n                    \u202f=\u202f0.438,\n                    <jats:italic>p<\/jats:italic>\n                    \u202f&amp;lt;\u202f0.001), and feedback literacy in turn predicted critical thinking (\n                    <jats:italic>\u03b2<\/jats:italic>\n                    \u202f=\u202f0.421,\n                    <jats:italic>p<\/jats:italic>\n                    \u202f&amp;lt;\u202f0.001). The indirect effect through feedback literacy was substantial (\n                    <jats:italic>\u03b2<\/jats:italic>\n                    \u202f=\u202f0.185, 95% CI [0.134, 0.254]; VAF\u202f=\u202f71.98%), and the direct effect dropped to non-significance once the mediator entered the model, a pattern consistent with near-complete mediation. Reflective thinking moderated the structure of the relationship: at low reflective thinking, GenAI use was directly associated with critical thinking; at high reflective thinking, the association was carried almost entirely by feedback literacy, and the conditional indirect effect was strongest. Because all variables were measured concurrently by self-report, the findings describe associations consistent with, but not demonstrative of, the proposed mechanism. They are nonetheless consistent with a view of GenAI use as a context-sensitive cognitive resource: its association with self-reported critical analytical skills depends on whether students can interpret, judge and act on AI-generated feedback, and whether they bring a reflective stance to the interaction. Implications for AI-pedagogy integration, feedback-literacy curricula and reflective course design in higher education are discussed.\n                  <\/jats:p>","DOI":"10.3389\/fpsyg.2026.1939286","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T05:41:04Z","timestamp":1790055664000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The association between generative AI use and university students\u2019 critical thinking: a moderated mediation model of GenAI feedback literacy and reflective thinking"],"prefix":"10.3389","volume":"17","author":[{"given":"Zhen","family":"Yan","sequence":"first","affiliation":[{"name":"Minzu Normal University of Xingyi","place":["Xingyi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuotao","family":"Fang","sequence":"additional","affiliation":[{"name":"Minzu Normal University of Xingyi","place":["Xingyi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weixing","family":"Zou","sequence":"additional","affiliation":[{"name":"Minzu Normal University of Xingyi","place":["Xingyi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"179","DOI":"10.3390\/a19030179","article-title":"A systematic literature review on the pedagogical implications and impact of GenAI on students\u2019 critical thinking","volume":"19","author":"Balart","year":"2026","journal-title":"Algorithms"},{"key":"ref2","first-page":"285","article-title":"The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration","volume":"2","author":"Barclay","year":"1995","journal-title":"Technol. 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