{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T20:28:03Z","timestamp":1790886483287,"version":"4.1.0"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"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:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Generative artificial intelligence (GenAI) has become embedded in higher education, raising concerns about students\u2019 behavioral dependency on these tools. Focusing on pre-service teachers\u2014who will shape how a future generation learns to use AI\u2014we asked how prevalent GenAI dependency is, what drives it, and whether perceived AI literacy protects against it.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In a mixed-methods design, 565 education-major students in Chinese teacher-preparation programs completed an online survey, and five were interviewed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Dependency markers were already widespread: 79% called AI indispensable to their learning, 78% felt unaccustomed without it, 54% \u201casked AI first,\u201d and 31% used its answers without verification. In the structural model (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u202f=\u202f0.60), the strongest drivers were\n                      <jats:italic>impulsivity<\/jats:italic>\n                      (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f0.40) and\n                      <jats:italic>anthropomorphic attachment<\/jats:italic>\n                      (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f0.28), with a smaller contribution from AI anxiety (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f0.15); trust was non-significant. Necessary condition analysis identified social norms as a necessary-in-degree condition. Perceived AI literacy was modestly\n                      <jats:italic>protective<\/jats:italic>\n                      (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f\u22120.19), stronger in the full model than its zero-order correlation (\n                      <jats:italic>r<\/jats:italic>\n                      \u202f=\u202f\u22120.14)\u2014a suppression effect, because more AI-literate students also trusted AI more and sat within more AI-reliant peer groups. Interviews converged with the survey model, most strikingly on anthropomorphic attachment, while qualifying the smaller anxiety pathway.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Dependency is driven chiefly by dispositional impulsivity and affective attachment rather than by low competence; interventions\u2014for policymakers, teacher educators, and pre-service teachers\u2014should prioritize self-regulation and awareness of parasocial engagement, treating AI-literacy training as a secondary lever.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1922716","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T04:46:13Z","timestamp":1786509973000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Addicted to AI? Prevalence and psychological drivers of generative AI dependency among pre-service teachers"],"prefix":"10.3389","volume":"17","author":[{"given":"Bin","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Educational Science, Qingdao University","place":["Qingdao, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Educational Science, Qingdao University","place":["Qingdao, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1037\/0033-2909.103.3.411","article-title":"Structural equation modeling in practice: a review and recommended two-step approach","volume":"103","author":"Anderson","year":"1988","journal-title":"Psychol. 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