Опубликована: Дек. 13, 2024
Язык: Английский
Опубликована: Дек. 13, 2024
Язык: Английский
Опубликована: Март 19, 2025
Collaboration has been shown to enhance creativity, leading more innovative and effective outcomes. While previous research explored the abilities of Large Language Models (LLMs) serve as co-creative partners in tasks like writing poetry or creating narratives, collaborative potential LLMs humor-rich culturally nuanced domains remains an open question. To address this gap, we conducted a user study explore co-creating memes - humor-driven specific form creative expression. We with three groups 50 participants each: human-only group without AI assistance, human-AI collaboration interacting state-of-the-art LLM model, AI-only where autonomously generated memes. assessed quality through crowdsourcing, each meme rated on humor, shareability. Our results showed that assistance increased number ideas reduced effort felt. However, it did not improve when humans collaborated LLM. Interestingly, created entirely by performed better than both all areas average. looking at top-performing memes, human-created ones were while collaborations stood out creativity These findings highlight complexities tasks. can boost productivity create content appeals broad audience, human crucial for connects deeper level.
Язык: Английский
Процитировано
1Kybernetes, Год журнала: 2025, Номер unknown
Опубликована: Янв. 1, 2025
Purpose Human-artificial intelligence (AI) collaboration, as a new form of cooperative interaction, has been applied in brainstorming activities. This study aims to explore the impact performance-reward expectancy (PRE) and creative motivation (CM), along with search for ideas associative memory (SIAM) theory, on participants' AI collaboration intent (AICI). Design/methodology/approach The research employs an online survey targeting users experience. Structural equation modeling (SEM) is analyze data validate proposed hypotheses. Findings PRE shows positive correlation both intrinsic (IM) extrinsic (EM). Furthermore, EM significantly positively influences AICI, while IM negative significant effect. Additionally, confirms mediating role social inhibition (SI) between AICI. Research limitations/implications examines collaborate brainstorming, filling gap existing research. It integrates SIAM theory how performance rewards influence this intent. reveal that performance-based effectively motivate engagement, but high may lead lower due autonomy concerns trust issues. emphasizes need open environment offers practical insights fostering addressing challenges like resistance among participants. Practical implications provides teams individuals, emphasizing importance integrating unlock its full potential. While are effective, still participants have attitudes toward collaboration. Creating inclusive essential. “individual + AI” model provoke highly intrinsically motivated participants, necessitating training improved transparency build trust. Although focused Chinese market, findings applicable globally, highlighting effective integration methods innovation. Social Our found can finding evidence our understanding mechanisms stimulating creativity. At same time, we also explored factors such production blocking affect individuals’ willingness work by influencing creativity motivation. better understand affects individual psychology team dynamics. These not only enrich innovation teamwork provide valuable references directions future Originality/value systematically CM within context AI-assisted first time. further investigates regulates process ultimately shapes engage offer theoretical guidance designing incentive enhance engagement AI-supported perspectives application
Язык: Английский
Процитировано
0Опубликована: Апрель 24, 2025
Generative AI has enabled novice designers to quickly create professional-looking visual representations for product concepts. However, novices have limited domain knowledge that could constrain their ability write prompts effectively explore a design space. To understand how experts and communicate about spaces, we conducted formative study with 12 experienced found -- less-versed clients often use references guide co-design discussions rather than written descriptions. These insights inspired DesignWeaver, an interface helps generate text-to-image model by surfacing key dimensions from generated images into palette quick selection. In 52 novices, DesignWeaver participants craft longer more domain-specific vocabularies, resulting in diverse, innovative designs. the nuanced heightened participants' expectations beyond what current models deliver. We discuss implications AI-based support tools.
Язык: Английский
Процитировано
0Опубликована: Март 18, 2025
Язык: Английский
Процитировано
0Journal of the Operational Research Society, Год журнала: 2025, Номер unknown, С. 1 - 19
Опубликована: Март 24, 2025
Язык: Английский
Процитировано
0Lecture notes on data engineering and communications technologies, Год журнала: 2025, Номер unknown, С. 19 - 30
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Опубликована: Апрель 23, 2025
Язык: Английский
Процитировано
0Опубликована: Апрель 23, 2025
Язык: Английский
Процитировано
0Опубликована: Апрель 24, 2025
Язык: Английский
Процитировано
0Опубликована: Апрель 24, 2025
Язык: Английский
Процитировано
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