{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T06:04:45Z","timestamp":1789452285417,"version":"build-2803163510"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"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>AI coding assistants are increasingly used in programming education, but willingness to use them does not show whether students evaluate or learn from AI suggestions. Grounded in technology acceptance, self-regulated learning, and self-efficacy theory, this study examined reflective AI use and student engagement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The Reflective AI Acceptance Integration Framework (RAIAF) organized three independent public datasets: a survey structural equation model (\n                      <jats:italic>N<\/jats:italic>\n                      = 131), double human coding of 66 nonblank Critical Engagement responses from a Copilot field study, and complementary programming-behavior prediction using DTA logs (\n                      <jats:italic>N<\/jats:italic>\n                      = 1, 423; 40% calendar-window sample\n                      <jats:italic>N<\/jats:italic>\n                      = 1, 122). The DTA dataset did not measure AI use and was used only to test whether recovery-related programming traces added predictive information.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the MLR survey model, technology acceptance predicted reflective use (\u03b2 = 0.694,\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.001) and engagement (\u03b2 = 0.694,\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.001); reflective use predicted programming self-efficacy (\u03b2 = 0.519,\n                      <jats:italic>p<\/jats:italic>\n                      &amp;lt; 0.001) and engagement (\u03b2 = 0.273,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.034). None of the hypothesized indirect effects was significant. Initial text-coder agreement was limited (linear-weighted \u03ba = 0.421; ordinal \u03b1 = 0.495), and consensus-coded reflective use did not significantly predict productive engagement (\n                      <jats:italic>b<\/jats:italic>\n                      = 0.149,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.202). In DTA, adding recovery features increased held-out final-success Macro-F1 from 0.725 to 0.743, but average gains in repeated and 30%\u201350% window analyses were negligible. Activity-defined engagement was already captured by overlapping activity indicators.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The findings distinguish acceptance from reflective use but do not establish a general mediation mechanism. Reflective use was associated with self-efficacy and engagement in the survey, whereas its text and behavioral evidence was exploratory, small, and context dependent.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1929887","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T05:33:34Z","timestamp":1789450414000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Reflective AI use and student engagement in AI-supported programming: technology acceptance, programming self-efficacy, and behavioral trace evidence"],"prefix":"10.3389","volume":"17","author":[{"given":"Sheng","family":"Wang","sequence":"first","affiliation":[{"name":"Shanghai University of Finance and Economics Zhejiang College","place":["Jinhua, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiabi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Management, Zhejiang University, Hangzhou","place":["Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"B1","first-page":"14","article-title":"\u201cMetatutor: a metacognitive tool for enhancing self-regulated learning,\u201d","volume-title":"Proceedings of the AAAI fall symposium on cognitive and metacognitive educational systems","author":"Azevedo","year":"2009"},{"key":"B2","volume-title":"Github Copilot in Computing Education: A Field Study Dataset","author":"Aziz","year":"2026"},{"key":"B3","doi-asserted-by":"publisher","first-page":"3","DOI":"10.5281\/zenodo.3554657","article-title":"The state of educational data mining in 2009: a review and future visions","volume":"1","author":"Baker","year":"2009","journal-title":"J. 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