{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T04:30:42Z","timestamp":1786595442326,"version":"build-2736575974"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"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":"\n Introduction<\/jats:title>\n Generative artificial intelligence (GenAI) can adapt English for Academic Purposes (EAP) reading materials by rewriting passages, adding support, or combining both. This study examined whether proficiency-sensitive GenAI adaptation chiefly changed passage-level structural complexity or text-embedded functional support while preserving academic fidelity.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n A role-prompted workflow combined barrier analysis, adaptation, fidelity checking, and validation. In a 3 \u00d7 3 between-subjects design (\n N<\/jats:italic>\n = 135;\n n<\/jats:italic>\n = 15 per cell), proficiency level (low, intermediate, and high) was crossed with material condition (original, unified-AI, and differentiated-AI). Three EAP instructors evaluated 15 anonymized material versions. Automated structural-complexity indicators, leave-one-out discriminant analysis, within-proficiency planned contrasts, and omnibus outcome models were used to assess material and learner outcomes.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The instructors rated the materials favorably for academic fidelity (\n M<\/jats:italic>\n = 4.24), proficiency appropriateness (\n M<\/jats:italic>\n = 4.36), and teachability (\n M<\/jats:italic>\n = 4.31), with acceptable inter-rater reliability,\n ICC<\/jats:italic>\n (2,\n k<\/jats:italic>\n ) = 0.84. Automated structural-complexity indicators showed limited separation (\n \n \n \n \n \n \u03b7<\/mml:mi>\n <\/mml:mrow>\n \n p<\/mml:mi>\n <\/mml:mrow>\n \n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msubsup>\n =<\/mml:mo>\n 0<\/mml:mn>\n .<\/mml:mo>\n 043<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ), and leave-one-out discriminant analysis classified the intended proficiency labels at 11.1%, below the 33.3% balanced-task benchmark. Material differences were clearest in functional support, including glosses, sentence unpacking, rhetorical cues, claim-evidence notes, and critical prompts. Differentiated-AI exceeded unified-AI most strongly among high-proficiency learners (\n d<\/jats:italic>\n = 1.40), followed by a moderate advantage among low-proficiency learners (\n d<\/jats:italic>\n = 0.60) and a small difference among intermediate learners (\n d<\/jats:italic>\n = 0.16). The omnibus reading-comprehension interaction identified this heterogeneity, F\n (4, 126)<\/jats:sub>\n = 7.43,\n p<\/jats:italic>\n &lt; 0.001, and\n \n \n \n \n \n \u03b7<\/mml:mi>\n <\/mml:mrow>\n \n p<\/mml:mi>\n <\/mml:mrow>\n \n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msubsup>\n =<\/mml:mo>\n 0<\/mml:mn>\n .<\/mml:mo>\n 191<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n . Supplementary process estimates were descriptive because the process indicators and outcomes were collected in the same session. Immediate unsupported application did not differ by material condition or interaction.\n <\/jats:p>\n <\/jats:sec>\n \n Discussion<\/jats:title>\n The findings locate GenAI-supported differentiation primarily in proficiency-specific support-layer design rather than broad changes in passage-level structural complexity. The strongest learner evidence came from comparisons conducted within the same proficiency level.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1887565","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T04:24:10Z","timestamp":1786595050000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From unified to differentiated materials: generative AI\u2013supported adaptation of EAP reading materials"],"prefix":"10.3389","volume":"17","author":[{"given":"Xuewei","family":"Gao","sequence":"first","affiliation":[{"name":"School of General Education (Public Art Education Center), Xihang University, Xi'an","place":["Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"12807","DOI":"10.18653\/v1\/2023.emnlp-main.790","article-title":"\u201cControlling pre-trained language models for grade-specific text simplification,\u201d","author":"Agrawal","year":"2023","journal-title":"Proceedings of the 2023 conference on empirical methods in natural language processing"},{"key":"B2","doi-asserted-by":"publisher","first-page":"6574","DOI":"10.18653\/v1\/2024.findings-acl.392","article-title":"\u201cText simplification via adaptive teaching,\u201d","author":"Bahrainian","year":"2024","journal-title":"Findings of the association for computational linguistics: ACL 2024"},{"key":"B3","doi-asserted-by":"publisher","first-page":"102411","DOI":"10.1016\/j.ijer.2024.102411","article-title":"ChatGPT in higher education: a systematic literature review and research challenges","volume":"127","author":"Baig","year":"2024","journal-title":"Int. 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