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dc.contributor.authorChen, Liuqing
dc.contributor.authorSun, Lingyun
dc.contributor.authorHan, Ji
dc.date.accessioned2023-11-29T18:58:02Z
dc.date.available2023-11-29T18:58:02Z
dc.date.copyright4/19/2023 12:00:00 AM
dc.date.issued4/19/2023 12:00:00 AM
dc.date.issued2023-04-19
dc.identifier.issn1530-9827
dc.identifier.otherjcise_23_5_051012.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4294495
dc.description.abstractCreativity is a fundamental feature of human intelligence. However, achieving creativity is often considered a challenging task, particularly in design. In recent years, using computational machines to support people in creative activities in design, such as idea generation and evaluation, has become a popular research topic. Although there exist many creativity support tools, few of them could produce creative solutions in a direct manner, but produce stimuli instead. DALL·E is currently the most advanced computational model that could generate creative ideas in pictorial formats based on textual descriptions. This study conducts a Turing test, a computational test, and an expert test to evaluate DALL·E’s capability in achieving combinational creativity comparing with human designers. The results reveal that DALL·E could achieve combinational creativity at a similar level to novice designers and indicate the differences between computer and human creativity.
dc.publisherThe American Society of Mechanical Engineers (ASME)
dc.titleA Comparison Study of Human and Machine-Generated Creativity
dc.typeJournal Paper
dc.journal.volume23
dc.journal.issue5
dc.journal.titleJournal of Computing and Information Science in Engineering
dc.identifier.doi10.1115/1.4062232
dc.journal.fristpage51012-1
dc.journal.lastpage51012-10
dc.page10
dc.treeJournal of Computing and Information Science in Engineering:;2023:;volume( 023 ):;issue: 005
dc.contenttypeFulltext


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