Evaluating the Effects of Personalised Learning on <scp>AI</scp> ‐Assisted Design Performance, Creative Self‐Efficacy, and Engagement of High‐ and Low‐Performing College Design Students

Abstract

ABSTRACT Background 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. Objectives This study explored how two generative AI models, ChatGPT 4 and DeepSeek, support high‐ and low‐performing college design students in personalised learning and influence their AI‐assisted design performance, creative self‐efficacy, and engagement. Methods This study adopts a comparative quasi‐experimental 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‐assisted learning conditions with distinct interaction styles and feedback structures. Results Results show that, compared with conventional creative methods, personalised learning based on both GAIs significantly improves student outcomes. ChatGPT users demonstrated higher creative self‐efficacy, while DeepSeek users exhibited greater engagement and AI‐assisted design performance. Although self‐efficacy and engagement were influenced by students' initial performance levels, AI‐assisted design performance improved consistently across groups. Semi‐structured interviews revealed that high‐performing students used ChatGPT to expand imagination and experiment with abstract concepts, while low‐performing students benefited from DeepSeek's structured prompts for clearer direction. Conclusions These different effects can be understood using Cognitive Load Theory, which shows that DeepSeek's organised outputs helped low‐performing students focus better, while ChatGPT's open‐ended feedback encouraged creative thinking for high‐performing learners. Overall, the findings underscore the necessity of differentiated AI integration strategies in design education, taking into account learner characteristics and instructional goals.

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