The Algorithmic Muse: Exploring AI's Impact on Human Creativity
DOI:
https://doi.org/10.56830/IJHMPS12202505Keywords:
Algorithmic, Artificial Intelligence, Human CreativityAbstract
Generative AI systems are now commonplace in artistic and educational creative practices, raising fundamental questions about human creativity, authorship, and collaboration. Rather than treating AI as a substitute for human creative potential, this research investigates how these technologies act as advanced creative partners that enhance rather than replace human intelligence. The findings indicate that creativity is fundamentally human and occurs through intentional acts that machines cannot fully replicate. However, AI systems add significant value by producing initial content—texts, images, and designs—that humans curate, edit, and integrate into final pieces of work. This human curation is what transforms an AI output into a genuinely creative outcome aligned with artistic objectives and user demands. The paper reviews three categories of creative acts in computer-aided design and establishes criteria for creativity attribution in human-AI partnerships: clarity of individual input, material effect on results, and proof of creative intent. The study argues that IP laws should treat human collaborators as real co-creators rather than just tool operators. There are six future research paths: algorithmic development, interdisciplinary integration, pedagogical reform, academia-industry alliance, empirical study, and ethical guidelines. In the end, the results point out that even if tech toolkits keep changing over time, human creativity still has its own special nature—this needs constant conversation to make sure AI use adds to our creative space instead of taking away from it
References
Beery, T., Stahl Olafsson, A., Gentin, S., Maurer, M., Stålhammar, S., Albert, C., et al. (2023). Disconnection from nature. Expanding our understanding of human–nature relations. People and Nature, 5(2), 470488. DOI: https://doi.org/10.1002/pan3.10451
Bilgram, V., & Laarmann, F. (2023). Accelerating innovation with generative AI: AI-augmented digital prototyping and innovation methods. IEEE Engineering Management Review, 51(2), 18-25. DOI: https://doi.org/10.1109/EMR.2023.3272799
Crimaldi, F., & Leonelli, M. (2023). AI and the creative realm. A short review of current and future applications.
Dainys, A. (2024). Human creativity versus machine creativity: will humans be surpassed by AI? Contemporaneous issues about creativity. DOI: https://doi.org/10.5772/intechopen.1007369
Dratler Jr, J., & McJohn, S. M. (2025). Intellectual property law: Commercial. creative and industrial property.
Epstein, Z., Hertzmann, A., Herman, L., Mahari, R., R. Frank, M., Groh, M., et al. (2023). Art and the science of generative AI. A deeper dive. DOI: https://doi.org/10.1126/science.adh4451
Esling, P., & Devis, N. (2020). Creativity in the era of artificial intelligence.
Inie, N., Falk, J., & Tanimoto, S. (2023). Designing Participatory AI. Creative Professionals' Worries and Expectations about Generative AI. DOI: https://doi.org/10.1145/3544549.3585657
Katsenou, R., Kotsidis, K., Papadopoulou, A., Anastasiadis, P., & Deliyannis, I. (2025). Beyond Assistance. Embracing AI as a Collaborative Co-Agent in Education, Education Sciences, 15(8), 1006. DOI: https://doi.org/10.3390/educsci15081006
Kurmi, R. K., Maurya, A., & Pujari, N. M. (2024). Nexus of artificial intelligence and human creativity: exploring opportunities and challenges. Journal of Drug Discovery and Health.
La Rosa, M. (2025). Contemporary Post-Production. Create, Cut, Collaborate, Color, Deliver. DOI: https://doi.org/10.4324/9781003473152
M. Darda, K., & S. Cross, E. (2022). The computer, A choreographer? Aesthetic responses to randomly-generated dance choreography by a computer. DOI: https://doi.org/10.31234/osf.io/yvgxk
Magnanini, S., Trabucchi, D., Buganza, T., & Verganti, R. (2022). Collaborate as a flock in the organization: how selection and synthesis influence knowledge convergence within a complex adaptive system. Journal of Knowledge Management, 26(11), 142-165. DOI: https://doi.org/10.1108/JKM-07-2021-0533
Pandy, G., Pugazhenthi, V. J., & Murugan, A. (2025). Generative AI: Transforming the Landscape of Creativity and Automation. International Journal of Computer Applications, 975, 8887. DOI: https://doi.org/10.5120/ijca2025924392
Qureshi, O. (2023). Artistic Innovation and Creativity: Driving Forces of Human Progress. Journal of Religion and Society.
Sarkar, A. (2023). Exploring Perspectives on the Impact of Artificial Intelligence on the Creativity of Knowledge Work. Beyond Mechanised Plagiarism and Stochastic Parrots. DOI: https://doi.org/10.1145/3596671.3597650
Sehgal, S. (2023). Towards the Ontological Unfolding of Generative AI: An Interdisciplinary Exploration of Creativity, Epistemology, and Ethics. Proceedings of the Generative Art Conference.
Tian, C., Cho, Y., Song, Y., Park, S., Kim, I., & Cho, S. Y. (2025). Integration of AI with artificial sensory systems for multidimensional intelligent augmentation. International Journal of Extreme Manufacturing, 7(4), 042002. DOI: https://doi.org/10.1088/2631-7990/adbd98
Todorov, P. (2019). A Game of Dice. Machine Learning and the Question Concerning Art.
Velardo, V., & Vallati, M. (2016). A General Framework for Describing Creative Agents.
Wang, H., Zou, J., Mozer, M., Goyal, A., Lamb, A., Zhang, L., et al. (2024). Can AI Be as Creative as Humans?





