{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T06:30:51Z","timestamp":1786516251450,"version":"build-2736575974"},"reference-count":39,"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\/"}],"funder":[{"DOI":"10.13039\/501100011997","name":"Dalian Polytechnic University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011997","id-type":"DOI","asserted-by":"publisher"}]}],"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>Artificial intelligence (AI) is increasingly embedded in university teaching, but the psychological processes through which faculty translate AI-related perceptions into AI-related teaching innovative behavior remain insufficiently understood. Drawing on the Job Demands\u2013Resources model, this study examines AI-enabled teaching resource compensation and professional identity threat as competing pathways linking perceived AI teaching integration to AI-related teaching innovative behavior. It further compares young and silver-aged faculty and uses configurational analysis to complement the variable-centered model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>An anonymous online survey was administered through Wenjuanxing to university faculty in China. The analytical sample comprised young faculty aged 45 years or below and silver-aged faculty aged 60 years or above. Structural equation modeling tested direct and mediating relationships and multigroup differences. Fuzzy-set qualitative comparative analysis examined configurations associated with high AI-related teaching innovative behavior.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Perceived AI teaching integration was positively associated with AI-related teaching innovative behavior. The resource-compensation pathway was the only statistically supported indirect pathway in the SEM model, whereas professional identity threat did not show a significant linear indirect association. Within the limits of a measurement model with modest absolute fit, the multigroup analysis did not provide evidence of significant between-cohort differences in the indirect effects. The exploratory fsQCA results suggested possible differences in the condition patterns associated with high AI-related teaching innovative behavior, but these patterns should be interpreted cautiously because the reported solution consistencies were moderate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings provide preliminary evidence that AI may be experienced as a teaching resource rather than as a direct professional identity threat in this sample. Universities may promote AI-supported teaching innovation by emphasizing pedagogical value, workload relief, and support conditions that are sensitive to faculty members' career-stage contexts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1917998","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:56:01Z","timestamp":1786514161000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Perceived AI teaching integration and AI-related teaching innovative behavior among young and silver-aged faculty in Chinese universities: resource compensation, professional identity threat, and configurational evidence"],"prefix":"10.3389","volume":"17","author":[{"given":"Nan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Marxism, Dalian Polytechnic University, Dalian","place":["Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuo","family":"Han","sequence":"additional","affiliation":[{"name":"Center for Teacher Education Research, Beijing Normal University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Center for Teacher Education Research, Beijing Normal University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Center for Teacher Education Research, Beijing Normal University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"B1","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\u2013step approach","volume":"103","author":"Anderson","year":"1988","journal-title":"Psychol. 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