Design education places strong emphasis on fostering students’ creativity, and the rapid development of embodied immersive technologies presents new opportunities in this regard. This exploratory study recruits 34 university students with different levels of expertise (17 novices and 17 experienced learners). Each participant completes a 3D conceptual design task using three design platforms with varying levels of embodiment: desktop, Mixed Reality (MR), and Virtual Reality (VR). Data on self-reported sense of embodiment, design creativity, and behavioral patterns are collected and analyzed. The results suggest that the VR platform, which provides the highest level of embodiment, is associated with significantly higher overall creativity as well as six creativity dimensions: fluency, flexibility, elaboration, originality, aesthetics, and requirement fulfillment. In addition, students using the VR platform exhibit more frequent and interconnected transitions between design behaviors, suggesting more exploratory and flexible design processes. No significant interaction is observed between design platform and expertise level in the present sample, suggesting that the effects of platform embodiment are broadly similar for novice and experienced learners. Based on these findings, this study proposes action-based metaphor and contextual consistency as potential explanatory factors that may contribute to creativity enhancement in highly embodied design platforms. Practical implications are discussed from the perspectives of platform design, learner differences, and pedagogical strategies. Overall, this exploratory study provides preliminary empirical evidence for understanding how embodied design platforms may support creativity in immersive design education and offers a foundation for future research.
Artificial intelligence is becoming deeply embedded in higher education, yet how the characteristics of AI-based e-learning systems relate to students’ perceived learning effectiveness and satisfaction remains insufficiently understood. This study examines the relationships of AI Functionality Compatibility, AI Instructional Process Coverage, and AI-Assisted Learning Cognitive Usability with Perceived Online Learning Effectiveness and Online Learning Satisfaction. Data from 384 students at Chinese universities were analyzed using a two-stage approach combining partial least squares structural equation modeling and artificial neural networks (PLS-SEM-ANN). The results showed that all three system characteristics were positively associated with perceived learning effectiveness, with instructional process coverage showing the strongest relationship. Cognitive usability also had a significant direct association with learning satisfaction, whereas functionality compatibility and instructional process coverage showed significant indirect effects through perceived learning effectiveness. The findings reveal an outcome-oriented pattern in which perceived learning effectiveness occupies a central position between system characteristics and satisfaction. This study extends understanding of AI-supported learning systems by emphasizing the alignment of technical functions with pedagogical processes and learners’ cognitive needs. It also provides practical guidance for universities and developers seeking to better align the design and evaluation of AI-based e-learning systems with learners’ instructional and cognitive needs.
Jia-Yuan Guo, Jiu-Yang Ren, Zhao-Lin Lu et al.· Systems· 0 citations
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