{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:35:19Z","timestamp":1792802119930,"version":"4.5.0"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"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>As generative artificial intelligence becomes integrated into higher education, teachers increasingly rely on AI text-detection reports to support judgments about authorship, writing quality, and academic integrity. Existing research has mainly examined detector accuracy, false positives, fairness, and policy; less is known about whether report design itself shapes teachers\u2019 evaluations when the judged text is unchanged. This gap matters because numerical scores and visual warnings may frame interpretation, anchor suspicion, and encourage confirmatory reading under uncertainty. Here, we tested how algorithmic warning strength and visual risk cues affect teachers\u2019 evaluations of student writing in a controlled single-stimulus experiment. In a 2 \u00d7 2 between-subjects experiment, 214 university teachers evaluated the same medium-quality Chinese social-science course paper accompanied by a fictitious AI-detection report that varied by AI detection rate (7% vs. 87%) and red-highlighting\/report-presentation package (absent vs. present). A high detection rate increased perceived AI authorship likelihood and risk and lowered overall quality evaluations, percentage-based scores, originality, language expression, and logical structure. Red highlighting also influenced report perception, language-expression judgments, and self-reported intervention tendency. Significant warning \u00d7 highlighting interactions emerged for percentage-based scoring, originality, language expression, logical structure, overall multidimensional quality, and intervention tendency, but not for the 1\u201310 overall rating or report perception. These preliminary and context-specific findings suggest that AI detection reports may function not merely as technical outputs but as socio-technical judgment environments under controlled evaluative conditions. Numerical warnings may anchor teachers\u2019 evaluations, while visual risk cues may selectively amplify suspicion and intervention-oriented responses. Responsible use of AI detection therefore requires neutral report design, independent teacher judgment, human oversight, and training on automation bias.<\/jats:p>","DOI":"10.3389\/fpsyg.2026.1889402","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:15:42Z","timestamp":1783437342000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Automation bias in teachers\u2019 evaluation of student writing: effects of algorithmic warnings and visual risk cues in AI detection reports"],"prefix":"10.3389","volume":"17","author":[{"given":"Peitao","family":"Du","sequence":"first","affiliation":[{"name":"Al-Farabi Kazakh National University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Liu","sequence":"additional","affiliation":[{"name":"Al-Farabi Kazakh National University","place":["Almaty, Kazakhstan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xujin","family":"Xian","sequence":"additional","affiliation":[{"name":"Department of Education, The Catholic University of Korea","place":["Bucheon, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s00146-019-00931-w","article-title":"In AI we trust? 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