{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:56:25Z","timestamp":1790751385510,"version":"4.1.0"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"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) can streamline information screening and integration in vocational learning, but easy access to fluent answers may reduce students\u2019 tendency to check sources. Drawing on a dual-process perspective, this study compared GenAI-only, conventional web-search, and hybrid-search conditions in terms of source verification, task trust, perceived cognitive load, and condition-supported immediate near-transfer performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Ninety-six vocational college students took part in a three-group, between-subjects eye-tracking experiment. After stratification by year of study, disciplinary cluster, and prior AI-use experience, they were randomly assigned to the three conditions (\n                      <jats:italic>n<\/jats:italic>\n                      = 32 each). Eye-tracking data, screen-recorded behavioral logs, questionnaires, and task scores were examined using between-group comparisons, correlation analyses, and hierarchical regression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      GenAI-only participants completed the two timed tasks fastest and reported the lowest perceived cognitive load and the highest task trust, but they devoted less attention to source evidence, made fewer cross-verification attempts, and revised their answers less often. Conventional web search involved more multi-source browsing and evidence comparison but required the most time. Hybrid search showed the highest source-evidence dwell proportion (27.9%), cross-verification-attempt rate (78.1%), and immediate near-transfer score (86.99), and outperformed both other conditions on near-transfer performance. After adjustment for search condition and prior tool experience, source-evidence dwell proportion (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      = 0.28,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.006) and cross-verification attempts (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      = 0.31,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.002) remained positively associated with near-transfer performance, whereas task trust did not.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The three search modes showed different trade-offs between efficiency, verification, and task performance. GenAI-only search reduced completion time and overall perceived cognitive load, whereas hybrid search was associated with more source checking and better immediate near-transfer performance under the assigned condition. These findings suggest that GenAI can support problem framing and information organization when learners continue to verify evidence and retain responsibility for final judgments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1946196","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:03:27Z","timestamp":1790748207000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Responsible use of generative AI in Chinese vocational education: eye-tracking evidence on source verification and immediate near-transfer across three information-seeking conditions"],"prefix":"10.3389","volume":"17","author":[{"given":"Ji","family":"Ma","sequence":"first","affiliation":[{"name":"School of Marxism, Liaoning Economy Vocational and Technical College","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjie","family":"Yue","sequence":"additional","affiliation":[{"name":"School of Marxism, Liaoning Economy Vocational and Technical College","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Design, East China Normal University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"ref1","author":"Amershi","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"17127","DOI":"10.1038\/s41598-024-67829-6","article-title":"Conversational presentation mode increases credibility judgements during information search with ChatGPT","volume":"14","author":"Anderl","year":"2024","journal-title":"Sci. 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