{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T02:01:19Z","timestamp":1776391279495,"version":"3.51.2"},"reference-count":98,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computer Assisted Learning"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In recent years, the integration of large language models has brought significant opportunities for advancing personalised learning in higher education. However, little attention has been paid to how students of different performance levels benefit from such tools, especially in creative disciplines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study explored how two generative AI models, ChatGPT 4 and DeepSeek, support high\u2010 and low\u2010performing college design students in personalised learning and influence their AI\u2010assisted design performance, creative self\u2010efficacy, and engagement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study adopts a comparative quasi\u2010experimental design to examine the differential effects of two generative AI tools, ChatGPT and DeepSeek, on personalised learning outcomes in design education. Rather than serving as a passive control, both tools function as AI\u2010assisted learning conditions with distinct interaction styles and feedback structures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Results show that, compared with conventional creative methods, personalised learning based on both GAIs significantly improves student outcomes. ChatGPT users demonstrated higher creative self\u2010efficacy, while DeepSeek users exhibited greater engagement and AI\u2010assisted design performance. Although self\u2010efficacy and engagement were influenced by students' initial performance levels, AI\u2010assisted design performance improved consistently across groups. Semi\u2010structured interviews revealed that high\u2010performing students used ChatGPT to expand imagination and experiment with abstract concepts, while low\u2010performing students benefited from DeepSeek's structured prompts for clearer direction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These different effects can be understood using Cognitive Load Theory, which shows that DeepSeek's organised outputs helped low\u2010performing students focus better, while ChatGPT's open\u2010ended feedback encouraged creative thinking for high\u2010performing learners. Overall, the findings underscore the necessity of differentiated AI integration strategies in design education, taking into account learner characteristics and instructional goals.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcal.70221","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T23:41:15Z","timestamp":1773358875000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the Effects of Personalised Learning on\n                    <scp>AI<\/scp>\n                    \u2010Assisted Design Performance, Creative Self\u2010Efficacy, and Engagement of High\u2010 and Low\u2010Performing College Design Students"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0129-4699","authenticated-orcid":false,"given":"Jiawei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Media and Technology Liaocheng University  Liaocheng China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6537-2334","authenticated-orcid":false,"given":"Jingru","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Art and Design Jiangsu Ocean University  Lianyungang China"}]},{"given":"Yuan","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Architecture and Design China University of Mining and Technology  Xuzhou China"}]}],"member":"311","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"Abdelghani R. 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