The Impact of Large Language Models (LLM) on Conceptual Approaches to Creativity
DOI:
https://doi.org/10.63437/3083-6433-2025-2(35)-17Keywords:
creativity, large language models, divergent thinking, convergent thinking, artificial intelligenceAbstract
This article provides a comprehensive theoretical analysis of how large language models (LLMs) transform contemporary approaches to defining creativity. The study draws on classical psychological, philosophical, and sociocultural theories, particularly concepts of divergent and convergent thinking and the distinction between P-creativity (psychological) and H-creativity (historical). The research employs conceptual analysis, integrating insights from psychology of creativity, philosophy of science, pedagogy, and AI studies to examine methodological shifts in understanding creativity as both psychological and sociocultural phenomenon.
The analysis reveals ambivalent effects of LLM integration into creative, educational, and scientific practices. While these models enhance fluency and short-term productivity, they introduce significant risks: homogenization of thinking through reproduction of dominant patterns, reduction of autonomous creative capacity, and transformation of authorship in human-machine collaboration. LLMs effectively support P-creativity by expanding users' cognitive space, yet their H-creativity capacity remains limited as they operate within existing linguistic patterns.
A critical finding concerns the validity crisis in traditional psychometric creativity assessment. Conventional instruments, including Torrance Tests, require methodological revision to account for algorithmic support. Quantitative increases in idea generation do not guarantee enhanced originality, as LLM outputs favor statistical probability over genuine innovation.
The paper outlines future research directions in developing process-oriented assessment models that account for human-AI interaction specifics in educational and scientific contexts.
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References
Використані літературні джерела
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References
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2. Torrance, E. P. (1974). Torrance tests of creative thinking. Lexington, MA, 79 p.
3. Boden, M. A. (2004). The creative mind: Myths and mechanisms. London, 360 p. DOI: https://doi.org/10.4324/9780203508527
4. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, 610-623. DOI: 10.1145/3442188.3445922.
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7. Acemoglu, D., & Johnson, S. (2023). Power and progress. New York, 560 p.
8. Mednick, S. A. (1962). The associative basis of the creative process. Psychological Review, 69(3), 220-232. DOI: 10.1037/H0048850.
9. Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), Article 248, 38 p. DOI: 10.1145/3571730.
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14. Torrance, E. P. (1962). Guiding creative talent. Englewood Cliffs, NJ, 278 p. DOI: https://doi.org/10.1037/13134-000
