Cognitive domestication in the age of algorithms: a study of the mechanisms by which AI divination influences the decision-making of Generation Z youth

Zhiman Cheng

doi:10.54254/2753-7102/2025.26541

Advances in Social Behavior ResearchOpen access

Cognitive domestication in the age of algorithms: a study of the mechanisms by which AI divination influences the decision-making of Generation Z youth

Research Article

Open Access

Cognitive domestication in the age of algorithms: a study of the mechanisms by which AI divination influences the decision-making of Generation Z youth

Zhiman Cheng ^1*

¹ College of Film, Television & Media, Guangxi Arts University

^*Corresponding author: 2572120714@qq.com

Published on 3 September 2025

ASBR Vol.16 Issue 7

ISSN (Print): 2753-7110

ISSN (Online): 2753-7102

Download Cover

Abstract

Against the backdrop of the deep integration of digital technology and youth subculture, AI tools such as DeepSeek have rapidly gained popularity among Generation Z, giving rise to a new cultural practice known as “AI divination.” This collective behavior reflects deeper cultural anxieties, as young people in the AI era constructed by algorithms are gradually losing their ability to engage with complex realities. How does AI divination reshape Generation Z's decision-making patterns? Does prolonged reliance on AI divination lead to a preference for simplistic attributions of complex realities, thereby eroding their capacity for deep thinking? Existing research has revealed the emotional motivations behind young people's online divination practices, but lacks in-depth exploration of how AI technology influences their decision-making. This study focuses on Gen Z's use of DeepSeek for AI divination, employing text analysis and in-depth interviews to investigate the mechanisms through which AI divination impacts young people's daily decision-making and the evolution of their cognitive patterns. The findings reveal that Generation Z's decision-making patterns exhibit a restructuring trend from deep thinking to algorithmic dependency. Through sustained interaction, young people's cognition undergoes a dynamic process of “cognitive domestication”. This study reveals the interactive relationship between technology and young people's cognition in the digital age, providing empirical evidence for understanding the digital transformation of Generation Z's subculture and the patterns of technology adoption.

Keywords:

AI divination, DeepSeek, cognitive domestication, Generation Z, youth subculture

View PDF

References

[1]. Xing, T. (2024). Dialogue with Oneself: Divination Subculture and the Spiritual World of Contemporary Youth.Contemporary Youth Research, (03), 113-124.

[2]. Flad, R. K. (2008). Divination and Power: A Multiregional View of the Development of Oracle Bone Divination in Early China.Current Anthropology, 49(3), 403–437. https: //doi.org/10.1086/588495

[3]. Lackner, M., & Lu, Z. (Eds.). (2022). The living traditions of divination. In Handbook of Divination and Prognostication in China (pp. 502–536).Brill. https: //doi.org/10.1163/9789004505231_018

[4]. Reigeluth, T., & Castelle, M. (2021). What kind of learning is machine learning? In J. Roberge & M. Castelle (Eds.), The Cultural Life of Machine Learning (pp. 79–115).Palgrave Macmillan. https: //doi.org/10.1007/978-3-030-56286-1_4

[5]. Chung, K., & Park, R. C. (2019). P2P Cloud Network Services for IoT Based Disaster Situations Information.Cluster Computing, 22(Suppl 1), 1925–1937. https: //doi.org/10.1007/s10586-017-1255-z [Note: Assuming relevance to ML/NLP advancement]

[6]. Wang, Y., Cai, X., Xu, C., & Li, J. J. (2021). How does AI-driven chatbot service influence customer engagement?Journal of Hospitality and Tourism Technology, 12(4), 609–623. https: //doi.org/10.1108/JHTT-10-2020-0254 [Note: Assuming relevance to ML/NLP/CAI]

[7]. Adamopoulou, E., & Moussiades, L. (2020). An Overview of Chatbot Technology. In Artificial Intelligence Applications and Innovations (pp. 373–383).Springer. https: //doi.org/10.1007/978-3-030-49186-4_31

[8]. Merritt, A. (2018, July 16). Here's what people are really doing with their Alexa and Google Home assistants.VentureBeat. https: //venturebeat.com/ai/heres-what-people-are-really-doing-with-their-alexa-and-google-home-assistants/

[9]. Daher, G., Brum, M., De Oliveira, R. R., et al. (2023). Generative AI in Medicine: Opportunities, Challenges and Ethical Considerations.Journal of Medical Systems, 47, 71. https: //doi.org/10.1007/s10916-023-01965-w [Note: Representative ref for GAI certainty/problem-solving]

[10]. Latif, E., Mai, G., Nyaaba, M., Wu, X., Liu, N., Lu, G., Li, S., Liu, T., & Zhai, X. (2023). Artificial General Intelligence (AGI) for Education.arXiv preprint arXiv: 2304.12479. https: //doi.org/10.48550/arXiv.2304.12479

[11]. Zhai, X., Nyaaba, M., & Ma, W. (2025). Can Generative AI and ChatGPT Outperform Humans on Cognitive-Demanding Problem-Solving Tasks in Science?Science & Education, 34, 649–670. https: //doi.org/10.1007/s11191-024-00496-1 [Note: Year adjusted to 2025 per DOI link]

[12]. High, A. C., & Caplan, S. E. (2009). Social anxiety and computer-mediated communication during initial interactions: Implications for the hyperpersonal perspective.Computers in Human Behavior, 25(2), 475–482. https: //doi.org/10.1016/j.chb.2008.10.011

[13]. Nomura, T., Kanda, T., Suzuki, T., & Kato, K. (2020). Psychology in Human–Robot Communication: An Attempt through Investigation of Negative Attitudes and Anxiety toward Robots.ROBOMECH Journal, 7, 4. https: //doi.org/10.1186/s40648-020-00156-3 [Note: Representative ref for social anxiety reduction with AI]

[14]. Ramadan, Z. (2021). "Alexafying" shoppers: The examination of Amazon's captive relationship strategy.Journal of Retailing and Consumer Services, 62, 102610. https: //doi.org/10.1016/j.jretconser.2021.102610

[15]. Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research.Academy of Management Annals, 14(2), 627–660. https: //doi.org/10.5465/annals.2018.0057

[16]. Klingbeil, A., Grützner, C., & Schreck, P. (2024). Trust and reliance on AI—An experimental study on the extent and costs of overreliance on AI.Computers in Human Behavior, 160, 108352. https: //doi.org/10.1016/j.chb.2024.108352 [Note: Year updated to 2024 per DOI]

[17]. Stolow, J. (2013). Introduction: Religion, technology, and the things in between. In J. Stolow (Ed.), Deus in Machina: Religion, Technology, and the Things in Between (pp. 1–22).Fordham University Press. https: //doi.org/10.5422/fordham/9780823244782.003.0001

[18]. Pasquinelli, M., & Joler, V. (2020). The Nooscope manifested: AI as instrument of knowledge extractivism.AI & Society, 36(4), 1263–1280. https: //doi.org/10.1007/s00146-020-01097-6 [p. 1276 cited]

[19]. Kruglanski, A. W., Webster, D. M., & Klem, A. (1993). Motivated resistance and openness to persuasion in the presence or absence of prior information.Journal of Personality and Social Psychology, 65(5), 861–876. https: //doi.org/10.1037/0022-3514.65.5.861

[20]. Komorita, S. S. (1995). Interpersonal relations: Mixed-motive interaction.Annual Review of Psychology, 46, 183–207. https: //doi.org/10.1146/annurev.ps.46.020195.001151

[21]. Kruglanski, A. W., & Webster, D. M. (1996). Motivated closing of the mind: "Seizing" and "freezing."Psychological Review, 103(2), 263–283. https: //doi.org/10.1037/0033-295X.103.2.263

[22]. Schrah, G. E., Dalal, R. S., & Sniezek, J. A. (2006). No decision-maker is an island: Integrating expert advice with information acquisition.Journal of Behavioral Decision Making, 19(1), 43–60. https: //doi.org/10.1002/bdm.511

[23]. Zhai, X. (2022). ChatGPT User Experience: Implications for Education.SSRN. https: //ssrn.com/abstract=4312418

[24]. Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration.Journal of Information Technology Case and Application Research, 25(3), 277–304. https: //doi.org/10.1080/15228053.2023.2233814

[25]. Chandra, S., Shirish, A., & Srivastava, S. C. (2022). To be or not to be… human? Theorizing the role of human-like competencies in conversational artificial intelligence agents.Journal of Management Information Systems, 39(4), 969–1005. https: //doi.org/10.1080/07421222.2022.2127458

[26]. Hao, X., Demir, E., & Eyers, D. (2024). Exploring collaborative decision-making: A quasi-experimental study of human and Generative AI interaction.Technology in Society, 78, 102662. https: //doi.org/10.1016/j.techsoc.2024.102662

[27]. Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M. K., Irshad, M., Arif, M., ... & Ahmad, T. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education.Humanities and Social Sciences Communications, 10(1), 1–17. https: //doi.org/10.1057/s41599-023-01787-8

[28]. Sarwat, S. (2018, December 26). Is AI making humans lazy? Here's what UAE residents say.Khaleej Times. https: //www.khaleejtimes.com/technology/is-ai-making-humans-lazy-heres-what-uae-residents-say [Note: Representative source for the cited point]

[29]. Krakauer, D. (2016, October 13). Will AI harm us? Better to ask how we'll reckon with our hybrid nature.Nautilus. http: //nautil.us/issue/41/selection/will-ai-harm-us

[30]. Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment.Organizational Behavior and Human Decision Processes, 151, 90–103. https: //doi.org/10.1016/j.obhdp.2018.12.005

[31]. Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence.AI & Society, 35(3), 611–623. https: //doi.org/10.1007/s00146-019-00931-w

[32]. Lai, Y., & Zhou, X. (2023). Trust Crisis or Algorithm Boost? An Investigation of Artificial Intelligence Divination Applications in China. In H. Degen & S. Ntoa (Eds.), Artificial Intelligence in HCI (pp. 517–529).Springer. https: //doi.org/10.1007/978-3-031-35894-4_38

[33]. Smith, R. J. (2021). Fortune-tellers and philosophers: Divination in traditional Chinese society.Routledge. [Note: Added as cited with Lackner & Lu, 2022]

[34]. Woodbine, O. (2011). Black Gods of the Asphalt: Religion, Hip-Hop, and Street Basketball.Journal of the American Academy of Religion, 79(4), 1130–1132. https: //doi.org/10.1093/jaarel/lfr070 [Note: Representative source for "app" example]

[35]. Ofuasia, E., & Ibiyemi, S. O. (2024). An Inquiry into the 'Maleness' of God in African Traditional Religions: The Igbo and Yorùbá.Journal of Africana Religions, 12(1), 86–103. https: //doi.org/10.5325/jafrireli.12.1.0086 [Note: Used as representative for "procedural steps" in digital divination]

[36]. Silverstone, R. (2003). Television and Everyday Life.Routledge. https: //doi.org/10.4324/9780203446915 [p. cited contextually for domestication theory]

[37]. Guitton, M. J. (2020). Cybersecurity, Social Engineering, Artificial Intelligence, Technological Addictions: Societal Challenges for the Coming Decade.Frontiers in Psychology, 11, 595913. https: //doi.org/10.3389/fpsyg.2020.595913 [Note: Representative for CAI compulsive dependence]

[38]. Ghosh, B., Daugherty, P. R., & Wilson, H. J. (2019). Taking a systems approach to adopting AI.Harvard Business Review. https: //hbr.org/2019/05/taking-a-systems-approach-to-adopting-ai [Note: Representative for AI substitution in decisions]

[39]. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making.Business Horizons, 61(4), 577–586. https: //doi.org/10.1016/j.bushor.2018.03.007