Generative AI as a Meta-Mediator in Creative Processes: A Vygotskian Perspective

Article Sidebar

PDF (Castellano) EPUB (Castellano)

Main Article Content

Cristóbal Araneda-Acuña
Universidad Diego Portales, Chile
https://orcid.org/0009-0007-7779-4012

This article explores the role of generative artificial intelligence (AI) in human creative processes from Vygotsky’s cultural-historical psychology perspective, positioning it as a “meta-mediator” that expands the traditional conception of dialectical mediation. It seeks to elucidate the concepts of mediation and creativity; explore the distinctive characteristics of AI as a mediator; delimit the possibility of applying the concept of Zone of Proximal Development (ZPD) for its understanding and systematize certain conditions that allow considering a collaboration as productive.
This study employs a theoretical framework based on a review of both Vygotskian literature and recent empirical studies on AI.
Our findings suggest that AI functions as a meta-mediator with apparent agency, expanding divergent thinking and fostering symbiotic collaborations with humans. This study differentiates between algorithmic and human creativity, arguing that Vygotsky’s Zone of Proximal Development (ZPD) concept is not applicable in this context. Instead, we propose a model that considers intention, predictability, and responsible expression as necessary factors for speaking of productive interactions with generative models.
The article applies Vygotskian theory to emergent AI, outlining conditions, from a cultural–historical standpoint, for integrating collaboration with AI while preserving human agency.

Keywords
Human-AI collaboration, Zone of Proximal Development, Mediation, Vygotsky, Cultural-historical psychology, Creativity, Generative AI

Article Details

How to Cite
Araneda-Acuña, Cristóbal. “Generative AI as a Meta-Mediator in Creative Processes: A Vygotskian Perspective”. Hipertext.net, 2025, no. 31, pp. 67-76, doi:10.31009/hipertext.net.2025.i31.07.
Rights
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

(c) Cristóbal Araneda-Acuña, 2025


Copyright

Author Biography

Cristóbal Araneda-Acuña, Universidad Diego Portales, Chile

Cristóbal Araneda-Acuña is graduate researcher at Diego Portales University (Chile) specializing in the intersection of behavioral sciences and data intelligence to design evidence-based social strategies. As an educational psychologist from the University of Chile, he has implemented emerging technologies —including AI and data analytics— to enhance community and educational programs across various institutions. His current research explores how to integrate these technological advances into public policy design, offering innovative, data-driven solutions to complex social challenges.

References

Akten, M. (2021). Deep Visual Instruments: Realtime Continuous, Meaningful Human Control over Deep Neural Networks for Creative Expression [Tesis Doctoral. Goldsmiths, University of London]. https://doi.org/10.25602/GOLD.00030191

Bangerl, M. M., Disch, L., David, T. y Pammer-Schindler, V. (2025). CreAItive Collaboration? Users’ Misjudgment of AI-Creativity Affects Their Collaborative Performance. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, article 195, 1–17. https://doi.org/10.1145/3706598.3713886

Bazerman, C. (2012). A Theory of Literate Action: Literate Action Volume 2. The WAC Clearinghouse; Parlor Press. https://doi.org/10.37514/per-b.2013.4791

Berti, L., Giorgi, F. y Kasneci, G. (2025). Emergent Abilities in Large Language Models: A Survey. arXiv, (arXiv:2503.05788). https://doi.org/10.48550/arxiv.2503.05788

Cai, L., Msafiri, M. M. y Kangwa, D. (2025). Exploring the impact of integrating AI tools in higher education using the Zone of Proximal Development. Education and Information Technologies, 30(6), 7191–7264. https://doi.org/10.1007/s10639-024-13112-0

Cai, W. y Gao, M. (2025). Beyond Hallucination: Generative AI as a Catalyst for Human Creativity and Cognitive Evolution. IECE Transactions on Emerging Topics in Artificial Intelligence, 2(1), 36–42. https://doi.org/10.62762/tetai.2025.657559

Chaiklin, S. (2003). The Zone of Proximal Development in Vygotsky’s Analysis of Learning and Instruction. En A. Kozulin, B. Gindis, V. S. Ageyev y and S. M. Miller (Eds.), Vygotsky’s Educational Theory in Cultural Context (pp. 39–64). Cambridge University Press. https://doi.org/10.1017/cbo9780511840975.004

Cole, M. (2003). Psicología cultural: Una disciplina del pasado y del futuro. Morata.

Cornock, S. y Edmonds, E. (1973). The Creative Process Where the Artist Is Amplified or Superseded by the Computer. Leonardo, 6(1), 11. https://doi.org/10.2307/1572419

Epstein, Z., Hertzmann, A., Herman, L., Mahari, R., Frank, M. R., Groh, M., Schroeder, H., Smith, A., Akten, M., Fjeld, J., Farid, H., Leach, N., Pentland, A. y Russakovsky, O. (2023). Art and the science of generative AI: A deeper dive. arXiv, (arXiv:2306.04141). https://doi.org/10.48550/arxiv.2306.04141

Eun, B. (2019). Two diverging paths toward a common destination: The dialogism of Vygotsky and Bakhtin. Culture & Psychology, 25(4), 484–502. https://doi.org/10.1177/1354067x18820635

Fadeev, A. (2022). Lev Vygotsky’s Approach in the Contemporary Semiotic Research of Learning, Meaning-Making and Inner Speech. University of Tartu Press.

Guitart, M. (2010). Los diez principios de la psicología histórico-cultural. Fundamentos En Humanidades, II(22), 45–60. https://www.redalyc.org/pdf/184/18419812003.pdf

Haase, J. y Pokutta, S. (2024). Human-AI Co-Creativity: Exploring Synergies Across Levels of Creative Collaboration. arXiv, (arXiv:2411.12527). https://doi.org/10.48550/arxiv.2411.12527

Hubert, K. F., Awa, K. N. y Zabelina, D. L. (2024). The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks. Scientific Reports, 14(1), 3440. https://doi.org/10.1038/s41598-024-53303-w

Kharkhurin, A. V. (2016). Cognitive Poetry: Theoretical Framework for the Application of Cognitive Psychology Techniques to Poetic Text. Creativity. Theories – Research - Applications, 3(1), 59–83. https://doi.org/10.1515/ctra-2016-0005

Knappe, J. (2024). Goetterfunke: Creativity in Machinae Sapiens. About the Qualitative Shift in Generative AI with a Focus on Text-To-Image. arXiv, (arXiv:2411.10448). https://doi.org/10.48550/arxiv.2411.10448

Li, J., Ouyang, J., Liu, J., Zhang, F., Wang, Z., Guo, X., Liu, M. y Taylor, D. (2023). Artificial Intelligence-based online platform assists blood cell morphology learning: A mixed-methods sequential explanatory designed research. Medical Teacher, 45(6), 596–603. https://doi.org/10.1080/0142159x.2023.2190483

Lu, S., Bigoulaeva, I., Sachdeva, R., Madabushi, H. T. y Gurevych, I. (2023). Are Emergent Abilities in Large Language Models just In-Context Learning? arXiv, (arXiv:2309.01809). https://doi.org/10.48550/arxiv.2309.01809

Mistarihi, M. Z. y M. A. (2025). Synergistic Minds: Exploring the Cultural Reimagining of Creativity and Problem-Solving Through Symbiotic Human-AI Teams in Advanced Manufacturing. Journal of Posthumanism, 5(6), 2169–2185. https://doi.org/10.63332/joph.v5i6.2325

Schaeffer, R., Miranda, B. y Koyejo, S. (2023). Are Emergent Abilities of Large Language Models a Mirage? Advances in Neural Information Processing Systems, 36, 55565–55581. https://proceedings.neurips.cc/paper_files/paper/2023/file/adc98a266f45005c403b8311ca7e8bd7-Paper-Conference.pdf

Shiiku, S., Marjieh, R., Anglada-Tort, M. y Jacoby, N. (2025). The Dynamics of Collective Creativity in Human-AI Social Networks. arXiv, (arXiv:2502.17962). https://doi.org/10.48550/arxiv.2502.17962

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. y Polosukhin, I. (2017). Attention Is All You Need. arXiv, (arXiv:1706.03762). https://doi.org/10.48550/arxiv.1706.03762

Veresov, N. (2004). Zone of proximal development (ZPD): the hidden dimension? En R. Heila-Ylikallio & A. Ostern (Eds.), Language as culture – tensions in time and space (Vol. 1, pp. 13–30). Vasa.

Veresov, N. (2017). ZBR and ZPD: Is there a difference? Cultural-Historical Psychology, 13(1), 23–36. https://doi.org/10.17759/chp.2017130102

Vygotsky, L. S. (2007). La tragedia de Hamlet, príncipe de Dinamarca, de W. Shakespeare & Psicología del arte (V. Imbert, Trans.). Fundación Infancia y Aprendizaje.

Vygotsky, L. S. (2009). El desarrollo de los procesos psicológicos superiores (M. Cole, V. John-Steiner, S. Scribner y E. Souberman, Eds.; S. Furió, Trans.). Crítica.

Wadinambiarachchi, S., Kelly, R. M., Pareek, S., Zhou, Q. y Velloso, E. (2024). The Effects of Generative AI on Design Fixation and Divergent Thinking. Proceedings of the CHI Conference on Human Factors in Computing Systems, article 380, 1–18. https://doi.org/10.1145/3613904.3642919

Wang, N. C. (2025). Scaffolding Creativity: Integrating Generative AI Tools and Real-World Experiences in Business Education. Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, article 468, 1–9. https://doi.org/10.1145/3706599.3720283

Weber, C. J., Burgkart, S. y Rothe, S. (2024). wr-AI-ter: Enhancing Ownership Perception in AI-Driven Script Writing. ACM International Conference on Interactive Media Experiences, 145–156. https://doi.org/10.1145/3639701.3656325

Zhang, X. y Zhou, Y. (2025). Human-AI Collaboration: Paradigm Shifts in Technology-Mediated Design. Art Sciences, 2(2), 1–8. https://doi.org/10.70267/as202502020108

Zhou, E. y Lee, D. (2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus, 3(3), pgae052. https://doi.org/10.1093/pnasnexus/pgae052