创新群体对人工智能创造性的评价及其影响:研究进展与未来展望
Innovative Groups’ Evaluation of Artificial Intelligence Creativity and Its Impact: Research Progress and Future Directions
摘要: 随着生成式人工智能在文本、图像、音乐与科研发现等领域的快速渗透,人工智能创作物呈现出更强的拟人化创造性特征,引发对人类主体性、协同创新与社会治理的双重关注。创新群体(科研人员、艺术家、设计师与AI研发者等)是最早接触并高频使用创造性AI的关键人群,其对AI创造性的评价与采用方式不仅影响人机协作效果,也会通过创新扩散作用影响公众态度与规范建构。本文综合心理学、计算创造学、人机交互与管理学等研究,系统梳理人工智能创造性的概念界定、测量范式与评价机制,归纳创新群体对AI创作物的感知、态度及其交互影响路径,并在此基础上提出未来研究议程,为负责任的人机协同创新提供参考。
Abstract: With the rapid proliferation of generative artificial intelligence (AI) across domains such as text production, visual art, music composition, and scientific discovery, AI-generated outputs increasingly exhibit anthropomorphic characteristics of creativity. This development has raised critical concerns regarding human agency, collaborative innovation, and societal governance. Innovative groups—including researchers, artists, designers, and AI developers—represent the earliest adopters and most frequent users of creative AI systems. Their evaluations of AI creativity and their patterns of adoption not only shape the effectiveness of human-AI collaboration but also influence public attitudes and normative frameworks through processes of innovation diffusion. Drawing upon research in psychology, computational creativity, human-computer interaction, and management science, this review systematically examines the conceptualization, measurement paradigms, and evaluative mechanisms of AI creativity. It synthesizes current findings on innovative groups’ perceptions, attitudes, and behavioral responses toward AI-generated works, and analyzes the interactive pathways through which these evaluations affect collaborative performance and social cognition. Based on this integrative framework, future research directions are proposed to advance responsible and sustainable human-AI co-creative innovation.
文章引用:侯佳林 (2026). 创新群体对人工智能创造性的评价及其影响:研究进展与未来展望. 心理学进展, 16(4), 252-258. https://doi.org/10.12677/ap.2026.164196

参考文献

[1] 陈凡, 吴怡(2022). 通用人工智能技术(AGI)的认识过程论探析. 系统科学学报, 30(4), 20-25.
[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.
[3] 蒋路远, 曹李梅, 秦昕, 谭玲, 陈晨, 彭小斐(2022). 人工智能决策的公平感知. 心理科学进展, 30(5), 1078.
[4] 蒋南平, 郑万军, 王竹君(2019). 人工智能与中国经济改革发展. 西南财经大学出版社.
[5] 刘强, 蒋芷翌(2019). 人工智能创作物独创性问题研究. 山东科技大学学报(社会科学版), 21(6), 39-50.
[6] 牟怡, 夏凯, Ekaterina Novozhilova, 许坤(2019). 人工智能创作内容的信息加工与态度认知——基于信息双重加工理论的实验研究. 新闻大学, (8), 30-43.
[7] 潘恩荣, 阮凡, 郭喨(2019). 人工智能“机器换人”问题重构: 一种马克思主义哲学的解释与介入路径. 浙江社会科学, (5), 93-99.
[8] 史忠植(2011). 高级人工智能. 科学出版社.
[9] 谢洪明, 陈亮, 杨英楠(2019). 如何认识人工智能的伦理冲突?——研究回顾与展望. 外国经济与管理, 41(10), 109-124.
[10] 易继明(2017). 人工智能创作物是作品吗? 法律科学(西北政法大学学报), 35(5), 137-147.
[11] 张兆翔, 张吉豫, 谭铁牛(2021). 人工智能伦理问题的现状分析与对策. 中国科学院院刊, 36(11), 1270-1277.
[12] 钟义信(2018). 机制主义人工智能理论——一种通用的人工智能理论. 智能系统学报, 13(1), 2-18.
[13] 朱晓妹, 王森, 何勤(2021). 人工智能嵌入视域下岗位技能要求对员工工作旺盛感的影响研究. 外国经济与管理, 43(11), 15-25.
[14] Atkinson, R. D. (2018). “It Is Going to Kill Us!” and Other Myths About the Future of Artificial Intelligence. IUP Journal of Computer Sciences, 12, 7-56.
https://www.proquest.com/openview/52adc006d08f330f3b324800b610fb59/1?pq-origsite=gscholar&cbl=2029993
[15] Beaty, R. E., & Johnson, D. R. (2021). Automating Creativity Assessment with SemDis: An Open Platform for Computing Semantic Distance. Behavior Research Methods, 53, 757-780.[CrossRef] [PubMed]
[16] Beaty, R. E., Johnson, D. R., Zeitlen, D. C., & Forthmann, B. (2022). Semantic Distance and the Alternate Uses Task: Recommendations for Reliable Automated Assessment of Originality. Creativity Research Journal, 34, 245-260.[CrossRef
[17] Boden, M. A. (2009). Computer Models of Creativity. AI Magazine, 30, 23-34.[CrossRef
[18] Burton, J. W., Stein, M., & Jensen, T. B. (2020). A Systematic Review of Algorithm Aversion in Augmented Decision Making. Journal of Behavioral Decision Making, 33, 220-239.[CrossRef
[19] C2PA Technical Working Group (2025). C2PA Content Credentials Explained: Addressing Common Questions and Updates. Coalition for Content Provenance and Authenticity (C2PA).
[20] Canaan, R., Menzel, S., Togelius, J., & Nealen, A. (2018). Towards Game-Based Metrics for Computational Co-Creativity. In 2018 IEEE Conference on Computational Intelligence and Games (CIG) (pp. 1-8). IEEE.[CrossRef
[21] Christensen, A. P., Cardillo, E. R., & Chatterjee, A. (2023). Can Art Promote Understanding? A Review of the Psychology and Neuroscience of Aesthetic Cognitivism. Psychology of Aesthetics, Creativity, and the Arts, 19, 1-13.
[22] Dang, J., & Liu, L. (2022). A Growth Mindset about Human Minds Promotes Positive Responses to Intelligent Technology. Cognition, 220, Article ID: 104985.[CrossRef] [PubMed]
[23] Doshi, A. R., & Hauser, O. P. (2024). Generative AI Enhances Individual Creativity but Reduces the Collective Diversity of Novel Content. Science Advances, 10, eadn5290.[CrossRef] [PubMed]
[24] Feldmann, H. (2013). Technological Unemployment in Industrial Countries. Journal of Evolutionary Economics, 23, 1099-1126.[CrossRef
[25] Gray, H. M., Gray, K., & Wegner, D. M. (2007). Dimensions of Mind Perception. Science, 315, 619-619.[CrossRef] [PubMed]
[26] Haase, J., Hanel, P. H. P., & Pokutta, S. (2025). Has the Creativity of Large-Language Models Peaked? An Analysis of Inter-and Intra-LLM Variability. Journal of Creativity, 35, Article ID: 100113.[CrossRef
[27] Helberger, N., & Diakopoulos, N. (2023). The European AI Act and How It Matters for Research into AI in Media and Journalism. Digital Journalism, 11, 1751-1760.[CrossRef
[28] Hernandez-Orallo, J. (2000). Beyond the Turing Test. Journal of Logic, Language and Information, 9, 447-466.[CrossRef
[29] Kenett, Y. N. (2019). What Can Quantitative Measures of Semantic Distance Tell Us about Creativity? Current Opinion in Behavioral Sciences, 27, 11-16.[CrossRef
[30] Köbis, N., & Mossink, L. D. (2021). Artificial Intelligence versus Maya Angelou: Experimental Evidence That People Cannot Differentiate AI-Generated from Human-Written Poetry. Computers in Human Behavior, 114, Article ID: 106553.[CrossRef
[31] Kurt, D. E. (2018). Artistic Creativity in Artificial Intelligence. Master Diss., Radbound University.
[32] Levesque, H. J., Davis, E., & Morgenstern, L. (2012). The Winograd Schema Challenge. In Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning (pp. 552-561). AAAI Press.
https://cdn.aaai.org/ocs/4492/4492-21843-1-PB.pdf
[33] Li, R., Zhu, C., Xu, B., Wang, X., & Mao, Z. (2025). Automated Creativity Evaluation for Large Language Models: A Reference-Based Approach. In Findings of the Association for Computational Linguistics: EMNLP 2025 (pp. 21475-21488). Association for Computational Linguistics.[CrossRef
[34] Liu, Y., Yang, Y., & Xu, H. (2025). From Humans to AI: Understanding Why AI Is Perceived as the Preferred Co-Creation Partner. Frontiers in Psychology, 16, Article ID: 1695532.[CrossRef
[35] Manyika, J. (2022). Getting AI Right: Introductory Notes on AI & Society. Daedalus, 151, 5-27.[CrossRef
[36] Mazzone, M., & Elgammal, A. (2019). Art, Creativity, and the Potential of Artificial Intelligence. Arts, 8, Article No. 26.[CrossRef
[37] McGuire, J., De Cremer, D., & Van de Cruys, T. (2024). Establishing the Importance of Co-Creation and Self-Efficacy in Creative Collaboration with Artificial Intelligence. Scientific Reports, 14, Article No. 18525.[CrossRef] [PubMed]
[38] Meincke, L., Nave, G., & Terwiesch, C. (2025). ChatGPT Decreases Idea Diversity in Brainstorming. Nature Human Behaviour, 9, 1107-1109.[CrossRef] [PubMed]
[39] Mokyr, J., Vickers, C., & Ziebarth, N. L. (2015). The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different? Journal of Economic Perspectives, 29, 31-50.[CrossRef
[40] Moruzzi, C., & Margarido, S. (2024). A User-Centered Framework for Human-AI Co-Creativity. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-9). ACM.[CrossRef
[41] Nass, C., & Moon, Y. (2000). Machines and Mindlessness: Social Responses to Computers. Journal of Social Issues, 56, 81-103.[CrossRef
[42] Neufeld, E., & Finnestad, S. (2020). Imitation Game: Threshold or Watershed? Minds and Machines, 30, 637-657.[CrossRef
[43] Ortiz, J., Marin, A., & Gualdron, O. (2016). Implementation of a Banking System Security in Embedded Systems Using Artificial Intelligence. Advances in Natural and Applied Sciences, 10, 95-101.
https://www.aensiweb.net/AENSIWEB/anas/anas/2016/December/95-101.pdf
[44] Runco, M. A., & Jaeger, G. J. (2012). The Standard Definition of Creativity. Creativity Research Journal, 24, 92-96.[CrossRef
[45] Saygin, A. P., Chaminade, T., Ishiguro, H., Driver, J., & Frith, C. (2012). The Thing that Should Not Be: Predictive Coding and the Uncanny Valley in Perceiving Human and Humanoid Robot Actions. Social Cognitive and Affective Neuroscience, 7, 413-422.[CrossRef] [PubMed]
[46] Schindler, I., Hosoya, G., Menninghaus, W., Beermann, U., Wagner, V., Eid, M. et al. (2017). Measuring Aesthetic Emotions: A Review of the Literature and a New Assessment Tool. PLOS ONE, 12, e0178899.[CrossRef] [PubMed]
[47] Stein, J., & Ohler, P. (2017). Venturing into the Uncanny Valley of Mind—The Influence of Mind Attribution on the Acceptance of Human-Like Characters in a Virtual Reality Setting. Cognition, 160, 43-50.[CrossRef] [PubMed]
[48] Stein, M. I. (1953). Creativity and Culture. The Journal of Psychology, 36, 311-322.[CrossRef
[49] Sundar, S. S. (2008). The MAIN Model: A Heuristic Approach to Understanding Technology Effects on Credibility (pp. 73-100). MacArthur Foundation Digital Media and Learning Initiative.
[50] Turing, A. M. (1950). I.—Computing Machinery and Intelligence. Mind, 59, 433-460.[CrossRef
[51] Van der Kaa, H. A. J., & Krahmer, E. J. (2014). Journalist versus News Consumer: The Perceived Credibility of Machine Written News. In Proceedings of the Computation + Journalism Conference (pp. 24-25).
[52] Wissing, B. G., & Reinhard, M. (2018). Individual Differences in Risk Perception of Artificial Intelligence. Swiss Journal of Psychology, 77, 149-157.[CrossRef
[53] Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S. et al. (2021). Artificial Intelligence: A Powerful Paradigm for Scientific Research. The Innovation, 2, Article ID: 100179.[CrossRef] [PubMed]
[54] Złotowski, J., Yogeeswaran, K., & Bartneck, C. (2017). Can We Control It? Autonomous Robots Threaten Human Identity, Uniqueness, Safety, and Resources. International Journal of Human-Computer Studies, 100, 48-54.[CrossRef