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Perceived Creativity and Evaluation Bias Toward Generative AI Artworks among University Students
Xiong Xiaoyan, Hou Jialin, Ding Youyin, Xiang Xuli, He Dong, Long Haiying, Chen Qunlin
Journal of Psychological Science ›› 2026, Vol. 49 ›› Issue (3) : 671-682.
PDF(1704 KB)
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Perceived Creativity and Evaluation Bias Toward Generative AI Artworks among University Students
Creativity has long been regarded as one of the advanced cognitive abilities unique to humans. Within the realm of creativity research, the perception of creativity in GAI-generated works and individuals’ attitudes to GAI creativity, particularly when these creations are judged to have creative characteristics, are key issues worthy of in-depth exploration. This study focuses on the artistic products generated by GAI, specifically three-line poems and oil paintings, and uses three progressive experiments to investigate individual perceptions and attitudes toward GAI creativity among college students. Additionally, the study explores key factors moderating human evaluations of GAI creations, aiming to provide empirical evidence to support the widespread adoption, evaluation, and societal utility of GAI.
In Experiment 1, a 2 (products’ actual author: human or GAI) × 2 (product quality: high or low) within-subjects design was employed. Participants’ likability of the products was considered as a covariate, and the perceived creativity of the products served as the dependent variable. During the experiment, participants viewed four three-line poems and four oil paintings, all created by humans or GAI. For each type of author, there was 1 high-creativity piece and 1 low-creativity piece. These works were presented in a random order within each type. The actual authorship of the works was hidden, and participants were asked to rate the works on a 7-point Likert scale for the creativity (obtained by calculating the average value of originality and appropriateness scores) and likability based on their subjective impressions. The results showed that the creative score of the three-line poems created by GAI was higher than that of those created by humans, but the main effect of the actual author of oil paintings was not significant. Overall, college students rated GAI’s works as equally creative as human-created works, though this rating was affected by the type and quality of the works.
In Experiment 2, a 2 (speculated author label: human or GAI) × 2 (product quality: high or low) within-subjects design was utilized. The dependent variables consisted of participants’ inferred authorship of the works and their perceptual evaluations (creativity and likability). During the experiment, participants were presented with four three-line poems and eight oil paintings. Among human-created works, there was 1 high-creativity and 1 low-creativity three-line poem, as well as 2 high-creativity and 2 low-creativity oil paintings; the categories and quantities of works created by GAI were consistent with those by humans. All works were displayed in a random order within each category (three-line poems and oil paintings). Without revealing the actual authorship, participants were asked to speculate on the authorship of each piece (Human or GAI) after viewing the works. Subsequently, participants rated the works on a 7-point Likert scale for creativity and likability. The results indicated that participants had moderate accuracy in distinguishing the creators of the three-line poems (54.64% accuracy), whereas their ability to identify the creators of the oil paintings (40.62% accuracy) was below chance level, suggesting an inability to accurately identify authorship. Whether they are three-line poems or oil paintings, the perceived score of the works created by the speculated author for “GAI” are always lower than those of the works created by the speculated author for “Human”. The above results show that for the three-line poems and oil paintings, whether the actual creator is GAI or humans, as long as the participants think the creator is GAI, their creativity ratings are lower. It indicates that people have speculative bias and evaluative bias on the creativity of GAI works.
In Experiment 3, a 2 (products’ actual author: human or GAI) × 2 (exoteric author label: human or GAI) within-subjects design was employed. Participants’ likability of the products was again considered as a covariate, and the perceived creativity of the products was the dependent variable. In this experiment, participants sequentially viewed eight three-line poems and eight oil paintings. The three-line poems and oil paintings were presented randomly within their respective categories. The exoteric author labels were displayed either at the top (for three-line poems) or at the bottom (for oil paintings) of the works. These labels were pseudo-randomized: within each pair of works from the same condition (the types and quality of the works are the same), one was randomly labeled as “Creator: Human/GAI”, while the other received the opposite label (Creator: GAI/Human). The remaining procedure adhered to the steps of Experiment 1. The results show that for both three-line poems and oil paintings, regardless of whether their actual author are GAI or humans, if the exoteric author label is “GAI”, their creativity ratings are lower. This suggests that evaluative bias persists even when authorship is explicitly disclosed. Furthermore, the evaluative bias was moderated by participants’ exposure to AI and their professional background. Specifically, evaluative bias was reduced in participants with higher levels of exposure to AI and those with a background in STEM fields.
In summary, this study highlights the perceptual characteristics of GAI creativity, the biases in college students’ perceptions of GAI-generated works, and the potential factors moderating these biases. The findings offer valuable insights into human-AI interactions and contribute to the advancement of human-computer collaboration and innovative behavior in the AI era.
generative artificial intelligence / creativity / speculative bias / evaluative bias
| [1] |
陈凡, 吴怡. (2021). 人工 “智” 能的智慧, 机智与明智. 自然辩证法通讯, 43(12), 95-100.
|
| [2] |
郭超, 鲁越, 林懿伦, 卓凡, 王飞跃. (2019). 平行艺术: 人机协作的艺术创作. 智能科学与技术学报, 1(4), 335-341.
近年来，机器艺术创作逐步得到了人们的重视和长足的发展，通过算法加工的作品，甚至纯粹由机器生成的作品越来越多地出现在人们的视野中。然而，这些作品在感官效果上与人类的艺术作品相去甚远，且不具备共情属性，因此难以被人类认可。与此同时，技术对艺术领域的冲击也引起了人们的担忧。针对目前机器和人在艺术创作中面临的技术问题和人机关系问题，提出了平行艺术理论体系。该体系旨在构建机器与人的伙伴关系，使人与机器在艺术创作中以平行的方式进行配合与协作。这也将为融合以人为主的情感关系与以机器为主的逻辑关系提供一个新思路。
|
| [3] |
姚若松, 梁乐瑶. (2010). 大五人格量表简化版 (NEO-FFI) 在大学生人群的应用分析. 中国临床心理学杂志, 18(4), 457-459.
|
| [4] |
衣新发, 林崇德, 蔡曙山, 黄四林, 陈桄, 罗良, 唐敏. (2011). 留学经验与艺术创造力. 心理科学, 34(1), 190-195.
|
| [5] |
周详, 祖冲, 崔虞馨. (2024). 创造力与人工智能. 陕西师范大学出版总社.
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
|
| [10] |
|
| [11] |
|
| [12] |
Despite abundant literature theorizing societal implications of algorithmic decision making, relatively little is known about the conditions that lead to the acceptance or rejection of algorithmically generated insights by individual users of decision aids. More specifically, recent findings of algorithm aversion-the reluctance of human forecasters to use superior but imperfect algorithms-raise questions about whether joint human-algorithm decision making is feasible in practice. In this paper, we systematically review the topic of algorithm aversion as it appears in 61 peer-reviewed articles between 1950 and 2018 and follow its conceptual trail across disciplines. We categorize and report on the proposed causes and solutions of algorithm aversion in five themes: expectations and expertise, decision autonomy, incentivization, cognitive compatibility, and divergent rationalities. Although each of the presented themes addresses distinct features of an algorithmic decision aid, human users of the decision aid, and/or the decision making environment, apparent interdependencies are highlighted. We conclude that resolving algorithm aversion requires an updated research program with an emphasis on theory integration. We provide a number of empirical questions that can be immediately carried forth by the behavioral decision making community.
|
| [13] |
|
| [14] |
|
| [15] |
|
| [16] |
|
| [17] |
|
| [18] |
|
| [19] |
|
| [20] |
|
| [21] |
|
| [22] |
Artwork is increasingly being created by machines through algorithms with little or no input from humans. Yet, very little is known about people’s attitudes and evaluations of artwork generated by machines. The current study investigates (a) whether individuals are able to accurately differentiate human-made artwork from AI-generated artwork and (b) the role of attribution knowledge (i.e., information about who created the content) in their evaluation and reception of artwork. Data was collected using an Amazon Turk sample from two survey experiments designed on Qualtrics. Findings suggest that individuals are unable to accurately identify AI-generated artwork and they are likely to associate representational art to humans and abstract art to machines. There is also an interaction effect between attribution knowledge and the type of artwork (representational vs. abstract) on purchase intentions and evaluations of artworks.
|
| [23] |
|
| [24] |
|
| [25] |
The recent advancements in the field of Artificial Intelligence (AI) have sparked a renewed interest in how organizations can potentially leverage and gain value from these technologies. Despite the considerable hype around AI, recent reports indicate that a very small number of organizations have managed to successfully implement these technologies in their operations. While many early studies and consultancy-based reports point to factors that enable adoption, there is a growing understanding that adoption of AI is rather more of a process of maturity. Building on this more nuanced approach of adoption, this study focuses on the diffusion of AI through a maturity lens. To explore this process, we conducted a two-phased qualitative case study to explore how organizations diffuse AI in their operations. During the first phase, we conducted interviews with AI experts to gain insight into the process of diffusion as well as some of the key challenges faced by organizations. During the second phase, we collected data from three organizations that were at different stages of AI diffusion. Based on the synthesis of the results and a cross-case analysis, we developed a capability maturity model for AI diffusion (AICMM), which was then validated and tested. The results highlight that AI diffusion introduces some common challenges along the path of diffusion as well as some ways to mitigate them. From a research perspective, our results show that there are some core tasks associated with early AI diffusion that gradually evolve as the maturity of projects grows. For professionals, we present tools for identifying the current state of maturity and providing some practical guidelines on how to further implement AI technologies in their operations to generate business value.
|
| [26] |
|
| [27] |
This essay discusses whether computers, using Artificial Intelligence (AI), could create art. First, the history of technologies that automated aspects of art is surveyed, including photography and animation. In each case, there were initial fears and denial of the technology, followed by a blossoming of new creative and professional opportunities for artists. The current hype and reality of Artificial Intelligence (AI) tools for art making is then discussed, together with predictions about how AI tools will be used. It is then speculated about whether it could ever happen that AI systems could be credited with authorship of artwork. It is theorized that art is something created by social agents, and so computers cannot be credited with authorship of art in our current understanding. A few ways that this could change are also hypothesized.
|
| [28] |
|
| [29] |
|
| [30] |
|
| [31] |
|
| [32] |
|
| [33] |
|
| [34] |
|
| [35] |
|
| [36] |
Psychometric scales are useful tools in understanding people’s attitudes towards different aspects of life. As societies develop and new technologies arise, new validated scales are needed. Robots and artificial intelligences of various kinds are about to occupy just about every niche in human society. Several tools to measure fears and anxieties about robots do exist, but there is a definite lack of tools to measure hopes and expectations for these new technologies. Here, we create and validate a novel multi-dimensional scale which measures people’s attitudes towards robots, giving equal weight to positive and negative attitudes. Our scale differentiates (a) comfort and enjoyment around robots, (b) unease and anxiety around robots, (c) rational hopes about robots in general (at societal level) and (d) rational worries about robots in general (at societal level). The scale was developed by extracting items from previous scales, crowdsourcing new items, testing through 3 scale iterations by exploratory factor analysis (Ns 135, 801 and 609) and validated in its final form of the scale by confirmatory factor analysis (N: 477). We hope our scale will be a useful instrument for social scientists who wish to study human-technology relations with a validated scale in efficient and generalizable ways.
|
| [37] |
In this study, we examined human reactions to other people’s experiences of using assistive robots at work. An online vignette experiment was conducted among respondents from the United States (N = 1059). In the experiment, participants read a written scenario in which another person had started using assistive robots to help with a daily work-related task. The experiment manipulated the closeness of the messenger (familiar versus unfamiliar colleague) and message orientation (positive versus negative). Finding out positive user experiences of a familiar or unfamiliar colleague increased positive attitude toward assistive robots, perceived robot usefulness, and perceived robot use self-efficacy. Furthermore, those who reported higher perceived robot suitability to one’s occupational field and openness to experiences reported more positive attitude toward assistive robots, higher perceived robot usefulness, and perceived robot use self-efficacy. The results suggest that finding out other people’s positive user experiences has a positive effect on perceptions of using assistive robots to help with a daily work-related task. Perceptions of assistive robots at work are also associated with individual and contextual factors such as openness to experiences and perceived robot suitability to one’s occupational field. This is one of the first studies to experimentally investigate the role of social influence in the perceptions of assistive robots at work.
|
| [38] |
|
| [39] |
|
| [40] |
|
| [41] |
|
| [42] |
|
| [43] |
Technology is widely considered the main source of economic progress, but it has also generated cultural anxiety throughout history. The developed world is now suffering from another bout of such angst. Anxieties over technology can take on several forms, and we focus on three of the most prominent concerns. First, there is the concern that technological progress will cause widespread substitution of machines for labor, which in turn could lead to technological unemployment and a further increase in inequality in the short run, even if the long-run effects are beneficial. Second, there has been anxiety over the moral implications of technological process for human welfare, broadly defined. While, during the Industrial Revolution, the worry was about the dehumanizing effects of work, in modern times, perhaps the greater fear is a world where the elimination of work itself is the source of dehumanization. A third concern cuts in the opposite direction, suggesting that the epoch of major technological progress is behind us. Understanding the history of technological anxiety provides perspective on whether this time is truly different. We consider the role of these three anxieties among economists, primarily focusing on the historical period from the late 18th to the early 20th century, and then compare the historical and current manifestations of these three concerns.
|
| [44] |
|
| [45] |
|
| [46] |
|
| [47] |
|
| [48] |
|
| [49] |
|
| [50] |
|
| [51] |
|
| [52] |
|
| [53] |
|
| [54] |
|
| [55] |
|
| [56] |
|
| [57] |
|
| [58] |
|
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