How Does the Use of Generative Artificial Intelligence Affect Employees’ Cognitive and Non-Cognitive Abilities? A Study Based on Social Learning Theory

Main Article Content

Yuhang Li
Department of Human Resources and Social Security, College of Public Administration, Nanjing Agricultural University, Nanjing 210095, China

DOI: https://doi.org/10.70267/aitia.20262642

Keywords

generative artificial intelligence, cognitive ability, non-cognitive ability, social learning theory

Abstract

Enterprises are vigorously promoting the deep integration of generative artificial intelligence into knowledge work scenarios, placing employees under realistic pressure regarding job adjustments. This pressure stems from AI beginning to intervene in employees’ cognitive processes, which may serve as a lever for capability enhancement or may induce job substitution. Against this backdrop, whether generative AI helps employees improve their abilities or encourages them to outsource their thinking processes has become a core issue that urgently needs to be addressed. Based on social learning theory, this study treats AI as a virtual modeler and empirically examines the impact of AI usage intensity on employees’ cognitive and non-cognitive abilities through an analysis of 677 questionnaire responses. The results show that AI usage intensity has a significant positive effect on employees’ cognitive abilities, specifically across five dimensions: knowledge comprehension, text processing, information gathering, office software application, and language expression. This effect further transfers to the personality level, significantly enhancing employees’ openness, extraversion, agreeableness, and conscientiousness, while inhibiting neurotic tendencies. Organizational type plays a moderating role, with the positive effect being significant only in non-public enterprises and institutions. This study extends social learning theory to the context of human–machine interaction, revealing the internalization pathway through which AI, as a virtual modeler, influences employees’ ability development. It also examines the transfer effect of capabilities from cognition to personality, providing theoretical support and practical implications for the capability-enhancing effects of human–machine collaboration.

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References

  • [1] Dong, Z., Aziz, M. F. A., & Wang, Y. (2025). An empirical analysis of how artificial intelligence development influences the adjustment of human capital structure. Finance Research Letters, 84.
  • [2] Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at Work. SSRN Electronic Journal.
  • [3] Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381, 187–192.
  • [4] Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing Work Productivity through Generative Artificial Intelligence: A Comprehensive Literature Review. Sustainability.
  • [5] Zhou, E., & Lee, D. (2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus, 3.
  • [6] Li, Y. H., & Xue, J. X. (2026). How does the use of generative artificial intelligence affect employees’ innovative performance? A heuristic-systematic perspective. Nankai Business Review, 1–39.
  • [7] Leonardi, P. M., Barley, S. R.. What’s under construction here? Academy of Management Annals, 2010, 4(1): 1–51.
  • [8] Gibson, S. (2004). Social Learning (Cognitive) Theory and Implications for Human Resource Development. Advances in Developing Human Resources, 6, 193–210.
  • [9] Teng, D., X. Zhou, H. Al-Samarraie, and L. Fang. 2026. Enhancing Critical Thinking and Self-Efficacy With GenAI: A Social Cognitive Perspective Using Structural Equation Modelling. Journal of Computer Assisted Learning, 42, no. 1: e70176.
  • [10] Egan, T., Yang, B., & Bartlett, K. (2004). The effects of organizational learning culture and job satisfaction on motivation to transfer learning and turnover intention. Human Resource Development Quarterly, 15, 279–301.
  • [11] Callari, T. C., & Puppione, L. (2025). Can Generative Artificial Intelligence Productivity Tools Support Workplace Learning? A Qualitative Study on Employee Perceptions in a Multinational Corporation. Journal of Workplace Learning. Advance online publication.
  • [12] Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., & Varma, A. (2023) Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3): 606–659.
  • [13] Vanneste, B. S., Puranam, P. (2024). Artificial intelligence, trust, and perceptions of agency. Academy of Management Review, Advance online.
  • [14] Liu, Y., Park, J., & McMinn, S. (2024). Using generative artificial intelligence/ChatGPT for academic communication: Students' perspectives. International Journal of Applied Linguistics. 34 (4), 1437–1461.
  • [15] Zhang, H., & Wang, Q. (2025). How could genai work on in-service teachers' knowledge building process? an empirical study based on epistemic network analysis. International Journal of Educational Technology in Higher Education, 22(1).
  • [16] Macnamara, B., Berber, I., Çavusoglu, M., Krupinski, E., Nallapareddy, N., Nelson, N., Smith, P., Wilson-Delfosse, A., & Ray, S. (2024). Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers’ awareness? Cognitive Research: Principles and Implications, 9.
  • [17] Wang, Y. Y., Huang, P. P., Jin, Y. H., Xu, L., & Wu, W. Y. (2024). Technological hollowing-out of knowledge workers in manufacturing under the background of artificial intelligence: Conceptualization, formation, and influence mechanisms. Advances in Psychological Science, 32(12), 2005–2017.
  • [18] Natali, C., Marconi, L., Duran, L., & Cabitza, F. (2025). AI-induced Deskilling in Medicine: A Mixed-Method Review and Research Agenda for Healthcare and Beyond. Artificial Intelligence Review, 58.
  • [19] Schmidt, M., Benzing, V., & Kamer, M. (2016). Classroom-Based Physical Activity Breaks and Children’s Attention: Cognitive Engagement Works. Frontiers in Psychology, 7.
  • [20] Egger, F., Benzing, V., Conzelmann, A., & Schmidt, M. (2019). Boost your brain, while having a break! The effects of long-term cognitively engaging physical activity breaks on children’s executive functions and academic achievement. PLoS ONE, 14.
  • [21] Hardy, J., Nelson, R., Thomason, M., Sternberg, D., Katovich, K., Farzin, F., & Scanlon, M. (2015). Enhancing Cognitive Abilities with Comprehensive Training: A Large, Online, Randomised, Active-Controlled Trial. PLoS ONE, 10.
  • [22] Von Bastian, C., Belleville, S., Udale, R., Reinhartz, A., Essounni, M., & Strobach, T. (2022). Mechanisms underlying training-induced cognitive change. Nature Reviews Psychology, 1, 30–41.
  • [23] Balinas, E. (2025). The Impact of Generative Artificial Intelligence on University Information Literacy Education: A Systematic Review from Challenges to Changes. International Journal of Latest Technology in Engineering Management & Applied Science.
  • [24] Heckman, J. J. (2011). Integrating personality psychology into economics. NBER Working Papers, 33(3): 1–31.
  • [25] Costa JR P T, McCrae R R. (1988). From Catalogue to Classification: Murray’s Needs and the Five-Factor Model. Journal of Personality and Social Psychology, 55(2): 258–265.
  • [26] Roberts, B. W., Kuncel, N. R., et al. (2007) The Power of Personality: The Comparative Validity of Personality Traits, Socioeconomic Status, and Cognitive Ability for Predicting Life Outcomes. Perspectives on Psychological Science, 2(4): 313–345.
  • [27] Specht, J., Egloff, B., & Schmukle, S. (2011). Stability and change of personality across the life course: the impact of age and major life events on mean-level and rank-order stability of the Big Five. Journal of Personality and Social Psychology, 101 (4), 862–82.
  • [28] Roberts, B. W., Luo, J., Briley, D. A., Chow, P. I., Su, R., & Hill, P. L. (2017). A systematic review of personality trait change through intervention. Psychological Bulletin, 143(2), 117.
  • [29] Bü hler, J., Orth, U., Bleidorn, W., Weber, E., Kretzschmar, A., Scheling, L., & Hopwood, C. (2023). Life Events and Personality Change: A Systematic Review and Meta-Analysis. European Journal of Personality, 38, 544–568.
  • [30] Woods, S. A., Lievens, F., Fruyt, F. D., & Wille, B. (2013). Personality across working life: the longitudinal and reciprocal influences of personality on work. Journal of Organizational Behaviour, 34(S1): S7–S25.
  • [31] Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2): 191-215.
  • [32] Caprara, G. V., Vecchione, M., Alessandri, G., Gerbino, M., & Barbaranelli, C. (2011). The contribution of personality traits and self-efficacy beliefs to academic achievement: a longitudinal study. British Journal of Educational Psychology, 81(1):78–96.
  • [33] Stajkovic, A. D., Bandura, A., Locke, E. A., Lee, D., & Sergent, K. (2018). Test of three conceptual models of the influence of the Big Five personality traits and self-efficacy on academic performance: a meta-analytic path analysis. Personality and Individual Differences, 120, 238–245.
  • [34] Furnham, A., & Cheng, H. (2023). The Big-Five personality factors, cognitive ability, health, and social- demographic indicators as independent predictors of self-efficacy: A longitudinal study. Scandinavian Journal of Psychology.
  • [35] Liang, J., Wang, L., Luo, J., Yan, Y., & Fan, C. (2023) The relationship between student interaction with generative artificial intelligence and learning achievement: Serial mediating roles of self-efficacy and cognitive engagement. Frontiers in Psychology, 14.
  • [36] Shahzad, M., Xu, S., & Zahid, H. (2024). Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education. Education and Information Technologies, 30, 3691–3716.
  • [37] Wang, X. H., &Yang, B. Y. (2007). The culture of learning organizations in Chinese state-owned and privately-owned enterprises: An empirical study. Frontiers of Business Research in China, 1(002), 275– 299.
  • [38] Ryan, R. M., & Deci, E. L.. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. The American Psychologist, 55(1), 68–78.
  • [39] Davidson, D. (1984). First Person Authority. Dialectica, 38 (2/3).
  • [40] Guo, L. Y. (Ed.). (2002). Psychology. Nanjing: Nanjing University Press.
  • [41] Credamo Insights. (2025). Technology Empowering Research, Education and Business. Monthly Journal of Technology Entrepreneurship, 38(08): 2.
  • [42] Williams, M., Corn, E., Ransanz, S., Berl, M., Andringa-Seed, R., & Mulkey, S. (2024). Neurodevelopmental assessments used to measure preschoolers' cognitive development in Latin America: a systematic review. Journal of Paediatric Psychology.
  • [43] R.B. Cattell. (1943). The description of personality: Basic traits resolved into clusters. The Journal of Abnormal and Social Psychology, 38 (4), 476–506.
  • [44] Wilhelm, O., Hildebrandt, A. and Oberauer, K. (2013) What is working memory capacity, and how can we measure it? Front. Psychol. 4:433.
  • [45] Rozgonjuk, D., Schmitz, F., Kannen, C., & Montag, C. (2021). Cognitive ability and personality: Testing broad to nuanced associations with a smartphone app. Intelligence, 88, 101578.
  • [46] Anderson, J. R. (2010). Cognitive Psychology and its Implications (7th ed.). Worth Publishers.
  • [47] Zhou,J.Y. (2020). The concept and measurement progress of non-cognitive skills. Global Education, 49(5), 53–66.
  • [48] Gosling, S. D., Rentfrow, P. J., Swann, W. B. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in Personality, 37(6), 504–528.
  • [49] Roccas, S., Sagiv, L., Schwartz, S. H., Knafo, A. (2002).The big five personality factors and personal values. Personality and Social Psychology Bulletin, 28(6), 789–801.
  • [50] Venkatesh, V., Morris, M., & Ackerman, P. (2000). A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision-Making Processes. Organizational Behavior and Human Decision Processes, 83 (1), 33–60.
  • [51] Liu, F. Y., & Hu, Z. Y. (2025). Generative artificial intelligence and employees' digital creativity. Journal of Technical Economics & Management, (10), 116–123.
  • [52] Kim, Y., Blazquez, V., & Oh, T. (2024). Determinants of Generative AI System Adoption and Usage Behavior in Korean Companies: Applying the UTAUT Model. Behavioral Sciences, 14.

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Published
Sep 13, 2026
Conference Proceedings Volume
Vol. 16 (2026): Proceedings of the 2026 International Conference on AI and Technological Innovation Applications (AITIA 2026)
Section
Articles
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How to Cite

Li, Yuhang. “How Does the Use of Generative Artificial Intelligence Affect Employees’ Cognitive and Non-Cognitive Abilities? A Study Based on Social Learning Theory”. Exploring Science Academic Conference Series, vol. 16, Sept. 2026, pp. 26-42, https://doi.org/10.70267/aitia.20262642.