{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:03:40Z","timestamp":1789707820951,"version":"4.0.1"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"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. Artif. Intell."],"abstract":"<jats:p>\n                    Conversational generative models are increasingly used to produce, transform, and revise information through multi-turn exchanges. However, large human\u2013AI datasets are still commonly analyzed through performance, preference, or content lenses, leaving conversation-level behavioral structure less operationalized. This study proposes a transparent rule-based framework for estimating persistence, delegation-related lexical patterns, and refinement-related lexical markers without inferring psychological dependence. LMSYS-Chat-1M and WildChat were analyzed as primary conversational corpora, while Chatbot Arena was included as a structurally conditional A\/B comparator. Across 1,879,085 valid analytical records, we calculated IPF, CDR, SRS, and the Conversational Behavioral Intensity Index (CBII), together with an equal-weight control. WildChat showed the highest mean CBII (0.2463), followed by LMSYS-Chat-1M (0.2123) and Chatbot Arena (0.1762). This ordering was stable under language controls, IPF thresholds of 5, 10, and 20 user turns, and question-level analysis of Chatbot Arena. Pairwise effect sizes were small to moderate, with the largest contrast between WildChat and Chatbot Arena (Cohen's\n                    <jats:italic>d<\/jats:italic>\n                    = 0.363).\n                  <\/jats:p>","DOI":"10.3389\/frai.2026.1914081","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:31:35Z","timestamp":1789705895000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Corpus-level behavioral intensity in human\u2013AI conversations"],"prefix":"10.3389","volume":"9","author":[{"given":"Aracely","family":"Mera-Navarrete","sequence":"first","affiliation":[{"name":"Escuela de Comunicaci\u00f3n, Facultad de Jurisprudencia, Ciencias Sociales y Humanidades Andr\u00e9s F. C\u00f3rdova, Universidad Internacional del Ecuador","place":["Quito, Ecuador"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Solange","family":"Revelo","sequence":"additional","affiliation":[{"name":"Unidad Educativa Particular San Gabriel","place":["Quito, Ecuador"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jefferson","family":"Beltr\u00e1n-Morales","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda y Ciencias Aplicadas, Universidad Central del Ecuador","place":["Quito, Ecuador"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Villegas-Ch","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda en Ciberseguridad, Facultad de Ingenier\u00eda y Ciencias Aplicadas, Universidad de Las Am\u00e9ricas","place":["Quito, Ecuador"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1568","DOI":"10.1057\/s41599-024-04044-8","article-title":"Trust in AI: progress, challenges, and future directions","volume":"11","author":"Afroogh","year":"2024","journal-title":"Humanit. 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