{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T15:22:37Z","timestamp":1788967357002,"version":"build-2803163510"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"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>This mixed-methods field study examined generative artificial intelligence (GenAI) pedagogical readiness among university faculty in five participating universities in China and Kazakhstan, with particular attention to cross-sample differences and the effects of faculty development training.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Baseline survey data were collected from 568 faculty members, including 340 from China and 228 from Kazakhstan. Readiness was assessed across seven dimensions: basic GenAI understanding, prompt design, AI-supported instructional design, AI-supported assessment, critical judgement, educational ethics, and disciplinary transfer. A quasi-experimental subsample of 160 faculty members compared structured GenAI prompt-task training with conventional GenAI-focused faculty development. Prompt logs were additionally coded to identify post-training faculty development needs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      At baseline, the Kazakhstani sample reported higher readiness across all seven dimensions, with the largest differences in disciplinary transfer, prompt design, and basic understanding. Measurement-invariance analysis supported cross-sample comparison of observed mean patterns. In hierarchical regression, the Kazakhstan coefficient decreased from\n                      <jats:italic>B<\/jats:italic>\n                      = 0.205 to\n                      <jats:italic>B<\/jats:italic>\n                      = 0.041 after prior GenAI use, recent AI-related training, multilingual resource access, institutional support, perceived permission, policy clarity, risk sensitivity, and disciplinary background were included. Post-test comparisons showed greater readiness in the structured prompt-task training group than in the conventional group, particularly for prompt design and critical judgement. Prompt-design gains remained above baseline at delayed testing. Prompt evidence linked these gains to objective specification, constraint setting, assessment criteria, faculty verification, and risk-control prompts.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The findings indicate that GenAI pedagogical readiness is shaped by faculty course tasks, institutional support, resource access, and training design. Structured prompt-task training may therefore provide a useful approach to strengthening course-level faculty readiness for pedagogical uses of GenAI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1890508","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T14:57:25Z","timestamp":1788965845000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on the optimization of the training system of university faculty development centers in the context of GenAI: a comparative analysis based on Chinese and Kazakhstani universities"],"prefix":"10.3389","volume":"17","author":[{"given":"Tao","family":"Bi","sequence":"first","affiliation":[{"name":"Al-Farabi Kazakh National University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shakirova","family":"Araily","sequence":"additional","affiliation":[{"name":"Al-Farabi Kazakh National University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anqi","family":"Lyu","sequence":"additional","affiliation":[{"name":"Al-Farabi Kazakh National University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Si","family":"Xiu","sequence":"additional","affiliation":[{"name":"Al-Farabi Kazakh National University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"100218","DOI":"10.1016\/j.caeai.2024.100218","article-title":"Bridging technology and pedagogy from a global lens: Teachers' perspectives on integrating chatgpt in english language teaching","volume":"6","author":"Al-khresheh","year":"2024","journal-title":"Comp. 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