Introducing the concept of relational processes in Human-AI creativity
Article Sidebar
Main Article Content
The integration of Generative Artificial Intelligence into creative processes raises scenarios that escape the classical notion of interaction between Humans and Computers. The emergence of co-creation, creative collaboration and distributed agency suggest that Human-AI creation is relational rather than interactive. This paper reviews this transition within different perspectives on humans and technologies for creativity and presents a theoretical contribution for the understanding of these processes, embracing the complexity of this new creative paradigm and interrelating cognitive, affective and behavioral dimensions. The paper conceptualizes relational processes in creativity as mutual influence processes in which Human and AI actors collaborate iteratively, sharing their agency and reciprocally modeling their behavior, knowledge structures, and affective responses. The concept is then applied to the audiovisual industry to explore the emerging dynamics in those processes while enabling a critical look at the implications of this new forms of creation regarding classical and new workflows and debates on labor and ethics. In the end, some conclusions are presented along with three initial research directions for relational processes in Human-AI creativity, highlighting the importance of raising critical awareness of these relationships in education.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
(c) Àlex Valverde-Valencia, 2025
Copyright
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).
Àlex Valverde-Valencia, Universitat Pompeu Fabra
Àlex Valverde-Valencia is a PhD researcher on Human-AI audiovisual creative processes and AI Literacy at Pompeu Fabra University Communication Department. Associate professor on Semiotics and Audiovisual Creation with Generative AI at UPF and BAU and trainer on Superior Teaching and Learning innovation. General Coordinator of the +RAIN Film Festival, the first AI Film festival in Europe.
Amankwah-Amoah, J., Abdalla, S., Mogaji, E., Elbanna, A. & Dwivedi, Y. K. (2024). The impending disruption of creative industries by generative AI: Opportunities, challenges, and research agenda. International Journal of Information Management, 79, 102759. https://doi.org/10.1016/j.ijinfomgt.2024.102759
Anantrasirichai, N. & Bull, D. (2022). Artificial intelligence in the creative industries: A review. Artificial Intelligence Review, 55(1), 589–656. https://doi.org/10.1007/s10462-021-10039-7
Antony, V. N. & Huang, C. M. (2025). ID.8: Co-creating visual stories with generative AI. ACM Transactions on Interactive Intelligent Systems, 14(3), 1–29. https://doi.org/10.48550/arXiv.2309.14228
Arévalo-Martínez, R. I., Flores, R. D. P. & Prado-Hurtado, R. I. (2025). Ethical considerations in the use of artificial intelligence in the audiovisual field. In The AI Revolution: How Technological Developments Affect the Audiovisual Sector (pp. 27–42). Springer Nature Switzerland. https://doi.org/10.4185/RLCS-2016-1121
Arrojo, M. J. (2024, September). Impact of artificial intelligence in the audiovisual industry: A taxonomy of uses and applications. In International Conference on Communication and Applied Technologies (pp. 183–194). Springer Nature Singapore. https://doi.org/10.1007/978-981-96-0426-5_16
Balzan, F., Munarini, M. & Angeli, L. (2024, July). Who pilots the copilots? Mapping a generative AI’s actor-network to assess its educational impacts. In International Conference on Artificial Intelligence in Education (pp. 448–456). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-64299-9_42
Bansal, G., Nushi, B., Kamar, E., Lasecki, W. S., Weld, D. S. & Horvitz, E. (2019, October). Beyond accuracy: The role of mental models in human-AI team performance. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 7, 2–11. https://doi.org/10.1609/hcomp.v7i1.5285
Bareis, J. & Katzenbach, C. (2022). Talking AI into being: The narratives and imaginaries of national AI strategies and their performative politics. Science, Technology, & Human Values, 47(5), 855–881. https://doi.org/10.1177/01622439211030007
Blok, V. (2022). The role of human creativity in human-technology relations. Philosophy & Technology, 35(3), 59. https://doi.org/10.1007/s13347-022-00559-7
Caballero, J. (2023). Hacia una nueva dimensión del montaje cinematográfico: explorando las posibilidades de la inteligencia artificial. Hipertext.net, (26), 53–58. https://doi.org/10.31009/hipertext.net.2023.i26.08
Caballero, J. & Sora-Domenjó, C. (2024). Automation and creativity in AI-driven film editing: The view from the professional documentary sector. Communication & Society, 37(3), 201–218. https://doi.org/10.15581/003.37.3.201-218
Caporusso, N. (2023). Generative artificial intelligence and the emergence of creative displacement anxiety. Research in Psychology and Behavior, 3(1). https://doi.org/10.53520/rdpb2023.10795
Caramiaux, B., Crawford, K., Liao, Q. V., Ramos, G. & Williams, J. (2025). Generative AI and creative work: Narratives, values, and impacts. arXiv, preprint arXiv:2502.03940. https://doi.org/10.48550/arXiv.2502.03940
Cave, S., Craig, C., Dihal, K., Dillon, S., Montgomery, J., Singler, B. & Taylor, L. (2018). Portrayals and perceptions of AI and why they matter. The Royal Society. https://doi.org/10.17863/CAM.34502
Celis Bueno, C., Chow, P. S. & Popowicz, A. (2025). Not “what”, but “where is creativity?”: Towards a relational-materialist approach to generative AI. AI & Society, 40(2), 339–351. https://doi.org/10.1007/s00146-024-01921-3
Cheng, Y., Xu, Z., Lin, D., Cheng, H., Wong, Y., Sun, Y. & Kankanhalli, M. (2024). Bridging the intent gap: Knowledge-enhanced visual generation. arXiv, preprint arXiv:2405.12538. https://doi.org/10.48550/arXiv.2405.12538
Chown, E. & Nascimento, F. (2022). Meaningful technologies: How digital metaphors change the way we think and live. Lever Press. https://doi.org/10.3998/mpub.12668201
Chung, J. J. Y., Kim, W., Yoo, K. M., Lee, H., Adar, E. & Chang, M. (2022, April). TaleBrush: Visual sketching of story generation with pretrained language models. In CHI Conference on Human Factors in Computing Systems Extended Abstracts, (pp. 1–4). https://doi.org/10.1145/3491102.3501819
Culpepper, M. (2018). I make, therefore I am: Agency, action, affordance, and the path to creative identity [Doctoral dissertation, University of Westminster]. https://doi.org/10.34737/q4z49
Davis, N. (2013). Human-computer co-creativity: Blending human and computational creativity. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 9(6), 9–12. https://doi.org/10.1609/aiide.v9i6.12603
Davis, N., Hsiao, C. P., Yashraj Singh, K., Li, L. & Magerko, B. (2016, March). Empirically studying participatory sense-making in abstract drawing with a co-creative cognitive agent. In Proceedings of the 21st International Conference on Intelligent User Interfaces, (pp. 196–207). https://doi.org/10.1145/2856767.2856795
Deshpande, M. & Magerko, B. (2024, May). Embracing embodied social cognition in AI: Moving away from computational theory of mind. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, article 119, 1–7. https://doi.org/10.1145/3613905.3650998
Epstein, Z., Hertzmann, A., Akten, M., Farid, H., Fjeld, J. & Smith, A. (2023). Art and the science of generative AI. Science, 380(6650), 1110–1111. https://doi.org/10.1126/science.adh4451
Gavran, I., Honcharuk, S., Mykhalov, V., Stepanenko, K. & Tsimokh, N. (2025). The impact of artificial intelligence on the production and editing of audiovisual content. Preservation, Digital Technology & Culture, 54(3), https://doi.org/10.1515/pdtc-2025-0022
Geroimenko, V. (2025). Generative AI: From human–computer interaction to human–computer creativity. In V. Geroimenko (Ed.). Human-Computer Creativity, (pp. 3 -29). Springer. https://doi.org/10.1007/978-3-031-86551-0_1
Gibson, J. J. (1977). The theory of affordances. In R. Shaw & J. Bransford (Eds.). Perceiving, Acting and Knowing (pp. 67–82). Lawrence Erlbaum Associates. https://doi.org/10.4324/9781315467931
Glăveanu, V. P. (2014). Distributed creativity: Thinking outside the box of the creative individual. Springer International Publishing. https://doi.org/10.1007/978-3-319-05434-6
Guzdial, M., Liao, N., Chen, J., Chen, S. Y., Shah, S., Shah, V. & Riedl, M. O. (2019, May). Friend, collaborator, student, manager: How design of an AI-driven game level editor affects creators. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, article 624, 1–13. https://doi.org/10.1145/3290605.3300854
Fuentes, G. & Omarova, A. (2024). Empowering the commons: Blockchain for IP protection in generative AI. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4803536
Hong, J. W. (2018, June). Bias in perception of art produced by artificial intelligence. In International Conference on Human-Computer Interaction, (pp. 290–303). Springer. https://doi.org/10.1007/978-3-319-91244-8_24
Ihde, D. (1990). Technology and the lifeworld: From garden to earth. Indiana University Press.
Jiang, H. H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D. & Gebru, T. (2023, August). AI art and its impact on artists. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, (pp. 363–374). https://doi.org/10.1145/3600211.3604681
Jiang, T., Sun, Z., Fu, S. & Lv, Y. (2024). Human-AI interaction research agenda: A user-centered perspective. Data and Information Management, 8(4), 100078. https://doi.org/10.1016/j.dim.2024.100078
Johnson-Laird, P. N. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness. Harvard University Press.
Kammersgaard, J. (1988). Four different perspectives on human–computer interaction. International Journal of Man-Machine Studies, 28(4), 343–362. https://doi.org/10.1016/S0020-7373(88)80017-8
Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University Press.
Lim, J., Leinonen, T., Lipponen, L., Lee, H., DeVita, J. & Murray, D. (2023). Artificial intelligence as relational artifacts in creative learning. Digital Creativity, 34(3), 192–210. https://doi.org/10.1080/14626268.2023.2236595
Lubart, T. I. (2001). Models of the creative process: Past, present and future. Creativity Research Journal, 13(3–4), 295–308. https://doi.org/10.1207/S15326934CRJ1334_07
Lubart, T. (2005). How can computers be partners in the creative process: Classification and commentary on the special issue. International Journal of Human-Computer Studies, 63(4–5), 365–369. https://doi.org/10.1016/j.ijhcs.2005.04.002
MacKenzie, I. S. (2024). Human-computer interaction: An empirical research perspective. Elsevier. https://doi.org/10.1016/C2022-0-02755-0
Maeda, T. & Quan-Haase, A. (2024, June). When human-AI interactions become parasocial: Agency and anthropomorphism in affective design. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, (pp. 1068–1077). https://doi.org/10.1145/3630106.3658956
Marrone, R., Cropley, D. & Medeiros, K. (2024). How does narrow AI impact human creativity? Creativity Research Journal, 1–11. https://doi.org/10.1080/10400419.2024.2378264
Martinez, L., Caramiaux, B. & Fdili Alaoui, S. (2025, April). Generative AI in documentary photography: Exploring opportunities and challenges for visual storytelling. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, article 329, 1–13. https://doi.org/10.1145/3706598.3714200
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(1), 18525. https://doi.org/10.1038/s41598-024-69423-2
McLuhan, M. (1994). Understanding media: The extensions of man. MIT Press.
Mitcham, C. (1994). Thinking through technology: The path between engineering and philosophy. University of Chicago Press.
Mohedas, S. D. & Alcarria, F. J. (2025a). La integración de inteligencia artificial generativa en el flujo de trabajo de postproducción audiovisual: El caso de La Mesías (Movistar Plus+, 2023). Prisma Social: Revista de Investigación Social, (48), 96–121. https://hdl.handle.net/10016/46199
Mohedas, S. D. & Alcarria, F. J. (2025b). Generative artificial intelligence in media production: The emerging role of artificial intelligence artist in Spain. Comunicação e Sociedade, 47, e025011. https://doi.org/10.17231/comsoc.47(2025).6212
Moruzzi, C. (2022). Creative agents: Rethinking agency and creativity in human and artificial systems. Journal of Aesthetics and Phenomenology, 9(2), 245–268. https://doi.org/10.1080/20539320.2022.2150470
Moruzzi, C. & Margarido, S. (2024, May). A user-centered framework for human-AI co-creativity. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, article 25, 1–9. https://doi.org/10.1145/3613905.3650929
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W. & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Oberting IV, V. A. (2024). Generative artificial intelligence and copyright in the film and media industry. Washington & Lee Law Review Online, 82(2), 123-171. https://scholarlycommons.law.wlu.edu/wlulr-online/vol82/iss2/2
Palani, S. & Ramos, G. (2024, June). Evolving roles and workflows of creative practitioners in the age of generative AI. In Proceedings of the 16th Conference on Creativity & Cognition, (pp. 170–184). https://doi.org/10.1145/3635636.3656190
Pandy, G., Pugazhenthi, V. J. & Murugan, A. (2025). Generative AI: Transforming the landscape of creativity and automation. International Journal of Computer Applications, 186(63), 7-13. https://doi.org/10.5120/ijca2025924392
Pedreschi, D., Pappalardo, L., Ferragina, E., Baeza-Yates, R., Barabási, A. L., Dignum, F. … & Vespignani, A. (2024). Human-AI coevolution. Artificial Intelligence, 339, 104244. https://doi.org/10.1016/j.artint.2024.104244
Peng, C., Zhang, S., Wen, F. & Liu, K. (2025). How loneliness leads to the conversational AI usage intention: The roles of anthropomorphic interface and parasocial interaction. Current Psychology, 44, 8177–8189. https://doi.org/10.1007/s12144-024-06809-5
Pyae, A. (2025). The human-AI handshake framework: A bidirectional approach to human-AI collaboration. arXiv preprint arXiv:2502.01493. https://doi.org/10.48550/arXiv.2502.01493
Rammert, W. (2008). Where the action is: Distributed agency between humans, machines, and programs. In U. Seifert, J. H. Kim & A. Moore (Eds.), Paradoxes of Interactivity (pp. 62–91). https://doi.org/10.14361/9783839408421-004
Rezwana, J. & Ford, C. (2025, June). Human-centered AI communication in co-creativity: An initial framework and insights. In Proceedings of the 2025 Conference on Creativity and Cognition, (pp. 651–665). https://doi.org/10.1145/3698061.3726932
Rezwana, J. & Maher, M. L. (2022, June). Understanding user perceptions, collaborative experience and user engagement in different human-AI interaction designs for co-creative systems. In Proceedings of the 14th Conference on Creativity and Cognition, (pp. 38–48). https://doi.org/10.1145/3527927.3532789
Rezwana, J. & Maher, M. L. (2023). Designing creative AI partners with COFI: A framework for modeling interaction in human-AI co-creative systems. ACM Transactions on Computer-Human Interaction, 30(5), 1–28. https://doi.org/10.1145/3519026
Richter, F. (2024). From human-system interaction to human-system co-action and back: Ethical assessment of generative AI and mutual theory of mind. AI and Ethics, 5(1), 19–28. https://doi.org/10.1007/s43681-024-00626-z
Schneider, J. (2024). Explainable generative AI (GenXAI): A survey, conceptualization, and research agenda. Artificial Intelligence Review, 57(11), 289. https://doi.org/10.1007/s10462-024-10916-x
Serena, A. (2025). The impact of generative AI on the film industry: Opportunities, challenges, and ethical implications [Doctoral dissertation, Politecnico di Torino]. https://webthesis.biblio.polito.it/id/eprint/35575
Silalahi, A. & Demirci, S. (2025). The paradox of frustration and anger in driving users’ continuance intention toward generative AI. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5231025
Shneiderman, B. (2002). Creativity support tools. Communications of the ACM, 45(10), 116–120. https://doi.org/10.1145/570907.570945
Simondon, G. (2017). On the mode of existence of technical objects. Univocal.
Stray, J. (2023). The AI learns to lie to please you: Preventing biased feedback loops in machine-assisted intelligence analysis. Analytics, 2(2), 350–358. https://doi.org/10.3390/analytics2020020
Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human–AI interaction (HAII). Journal of Computer-Mediated Communication, 25(1), 74–88. https://doi.org/10.1093/jcmc/zmz026
Tallala, T. (2024). Generative artificial intelligence, content creation and intellectual property rights in commercial use: Crediting the audiovisual output. [Bachelor’s Thesis, Turku University of Applied Sciences]. https://urn.fi/URN:NBN:fi:amk-2024062623863
Taffel, S. (2019). Automating creativity. Spheres: Journal for Digital Cultures, (5), 1–9. https://doi.org/10.25969/mediarep/13495
Turchi, T., Carta, S., Ambrosini, L. & Malizia, A. (2023). Human-AI co-creation: Evaluating the impact of large-scale text-to-image generative models on the creative process. In L. D. Spano, A. Schmidt, C. Santoro & S. Stumpf (Eds.), End-User Development. Lecture Notes in Computer Science, 13917. Springer. https://doi.org/10.1007/978-3-031-34433-6_3
Valverde-Valencia, À. (2023). Self-effects in AI-mediated communication. Hipertext.net, (26), 47–52. https://doi.org/10.31009/hipertext.net.2023.i26.07
Verbeek, P. P. (2005). What things do: Philosophical reflections on technology, agency, and design. Penn State Press.
Viñolo Locubiche, S. (2024). AI and the development of new audiovisual narratives. In R. V. Benítez-Rojas & F-J Martínez-Cano (Eds.). Revolutionizing Communication: The Role of Artificial Intelligence (pp. 37–47). Springer. https://doi.org/10.1201/9781003473633
Voinea, D. V. (2025). The algorithmic auteur: AI, cultural production, and the reconfiguration of audiovisual media. Social Sciences and Education Research Review, 12(1), 256–268. https://doi.org/10.5281/zenodo.15804554
Voynov, A., Aberman, K. & Cohen-Or, D. (2023, July). Sketch-guided text-to-image diffusion models. In ACM SIGGRAPH 2023 Conference Proceedings, article 55, 1–11. https://doi.org/10.1145/3588432.3591560
Wan, Q., Hu, S., Zhang, Y., Wang, P., Wen, B. & Lu, Z. (2024). “It felt like having a second mind”: Investigating human-AI co-creativity in prewriting with large language models. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW1), article 84, 1–26. https://doi.org/10.1145/3637361
Wang, B., Han, J., Zhao, X., Yin, Y., Chen, L. & Childs, P. (2025). Creative combinational design through generative AI in different dimensional representations: An exploration. Design and Artificial Intelligence, 1(1), 100006. https://doi.org/10.1016/j.daai.2025.100006
Wang, D., Churchill, E., Maes, P., Fan, X., Shneiderman, B., Shi, Y. & Wang, Q. (2020, April). From human-human collaboration to human-AI collaboration: Designing AI systems that can work together with people. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 1–6. https://doi.org/10.1145/3334480.3381069
Xu, W., Dainoff, M. J., Ge, L. & Gao, Z. (2021). From human-computer interaction to human-AI interaction: New challenges and opportunities for enabling human-centered AI. arXiv preprint arXiv:2105.05424. https://doi.org/10.48550/arXiv.2105.05424
Zhang, C., Wang, W., Pangaro, P., Martelaro, N. & Byrne, D. (2023, June). Generative image AI using design sketches as input: Opportunities and challenges. In Proceedings of the 15th Conference on Creativity and Cognition, 254–261. https://doi.org/10.1145/3591196.3596820
Zhu, F. & Zou, W. (2023). The role of generative AI in human creative processes: Experimental evidence. SSRN Electronic Journal. https://dx.doi.org/10.2139/ssrn.4676053