{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T06:07:36Z","timestamp":1789106856998,"version":"build-2803163510"},"reference-count":69,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"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>While student-AI interaction patterns have been explored in self-paced cross-linguistic tasks such as L2 writing and translation post-editing, little is known about how students interact with AI in bilingual tasks under intense cognitive and temporal constraints. This study uses computer-assisted consecutive interpreting (CACI) as a window to examine student-AI interaction patterns in such high-stakes environments. Twenty-two Chinese-native interpreting trainees, grouped by prior AI training experience, performed bidirectional CACI tasks using AI-enabled systems integrating automatic speech recognition (ASR) and machine translation (MT) features. With data collected from eye-tracking, pen-recording, and voice-recording, the study reveals that: (a) four interpretable interaction profiles emerge: Intensive Engagers (heavy AI reliance), Fast Scanners (scanning-based processing), Traditionalists (minimal AI reliance), and Frequent Switchers (shifts between AI support and handwritten notes); (b) these patterns shift dynamically between the comprehension and production stages of interpreting, while targeted CACI training was associated with more stable, AI-oriented patterns; and (c) students\u2019 interaction patterns during the comprehension stage, but not the production stage, are significantly associated with performance quality. These findings highlight the heterogeneity and variability of student-AI interaction patterns across different stages of complex bilingual tasks and underscore training effects on shaping students\u2019 behaviors and performance.<\/jats:p>","DOI":"10.3389\/fpsyg.2026.1890808","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:30:07Z","timestamp":1789104607000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Student-AI interaction in computer-assisted consecutive interpreting: patterns and performance"],"prefix":"10.3389","volume":"17","author":[{"given":"Huolingxiao","family":"Kuang","sequence":"first","affiliation":[{"name":"School of Foreign Languages, Renmin University of China","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Language Science and Technology, The Hong Kong Polytechnic University","place":["Hong Kong, Hong Kong SAR, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Weng","sequence":"additional","affiliation":[{"name":"Department of Language Science and Technology, The Hong Kong Polytechnic University","place":["Hong Kong, Hong Kong SAR, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1075\/intp.10.2.03alb","article-title":"(non-)sense in note-taking for consecutive interpreting","volume":"10","author":"Albl-Mikasa","year":"2008","journal-title":"Interpreting"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"977","DOI":"10.3758\/BRM.41.4.977","article-title":"Moving beyond Ku\u010dera and Francis: a critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English","volume":"41","author":"Brysbaert","year":"2009","journal-title":"Behav. 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