{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T12:31:13Z","timestamp":1789129873185,"version":"build-2803163510"},"reference-count":47,"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:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI)-engaged adaptive studying platforms are being rapidly adopted across higher education. While they provide significant opportunities to tailor students with both educational content and learning paces to individual needs, educators and researchers proposed their concerns that over-reliance on algorithmic itself may unconsciously be negatively associated with students\u2019 ability in self-regulated learning (SRL)\u2014a phenomenon termed the \u201cpersonalization paradox.\u201d<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Integrating control-value theory (CVT) and self-regulated learning (SRL) theory, this study demonstrates that academic emotions\u2014enjoyment, anxiety, and boredom\u2014mediate the relationship between perceived AI-adaptive environments and SRL, with CVT clarifying why these environments foster emotional responses and SRL theory explaining how such emotions translate into behavioral outcomes. This study uniquely combines CVT and SRL within a single moderated mediation structure, providing an innovative theoretical lens to know the personalization paradox very well.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A three-wave longitudinal survey was applied across one semester during the academic year with 486 undergraduates from four universities in China. At T1, students reported their perceptions of the AI-adaptive surroundings and AI literacy to researchers; at T2, they reported academic emotions; at T3, they reported their SRL behaviors. Structural equation modeling with latent interaction terms and cluster-robust standard errors were utilized to estimate the moderated mediation structure.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Perceived AI-adaptive environments were negatively related to SRL (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f\u22120.19,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.01), giving certain supports to personalization paradox. Indirect effects in enjoyment (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f\u22120.08), anxiety (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f\u22120.06), and boredom (\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f\u22120.09) were completely significant, respectively accounting for 54.8% of the entire effect. Most importantly, AI literacy moderated the first selective pathways from the AI environment to every academic emotion (e.g., enjoyment was\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u202f=\u202f0.14,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.01). such that the negative emotional impacts were moderated among students with higher AI literacy. This model accounted for 28.4% of the variance in SRL.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The personalization paradox operates through affective pathways, with AI literacy playing the role as a significant protective index. These results highlight the emotional and autonomy-related trade-offs inherent in algorithmic personalization and suggest that universities embed AI literacy as a credit-bearing module and design adaptive systems with lucid, user-controllable recommended interfaces. Causal inferences still are limited given the existing observational design.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1915839","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T11:37:06Z","timestamp":1789126626000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The personalization paradox: how AI-driven adaptive learning environments are associated with college students\u2019 academic emotions and self-regulation learning\u2014a moderated mediation model"],"prefix":"10.3389","volume":"17","author":[{"given":"Jing","family":"Li","sequence":"first","affiliation":[{"name":"School of Physics and Electronic Engineering, Yancheng Teachers University","place":["Yancheng, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziheng","family":"Lin","sequence":"additional","affiliation":[{"name":"Graduate School of Zhejiang University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengqun","family":"Qiu","sequence":"additional","affiliation":[{"name":"Innovation Entrepreneurship College, Yancheng Teachers University","place":["Yancheng, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref1","first-page":"432","volume-title":"Handbook of Self-Regulation of Learning and Performance","author":"Azevedo","year":"2022"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.chb.2019.03.025","article-title":"Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: issues and challenges","volume":"96","author":"Azevedo","year":"2019","journal-title":"Comput. 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