The Role of Artificial Intelligence in Film Production
DOI:
https://doi.org/10.55544/ijrah.6.4.49Keywords:
Artificial Intelligence, Cinema, Generative Artificial Intelligence, Film Production Phases, Advantages, ChallengesAbstract
In recent years, artificial intelligence has emerged as one of the driving forces reshaping the film industry, leaving scarcely any stage of the filmmaking process untouched. This paper sets out to examine the role and function of artificial intelligence across the three principal phases of cinematic production—pre-production, production, and post-production. To this end, a library-based, descriptive-analytical method was adopted, whereby relevant information was gathered through peer-reviewed scholarly articles, books, and specialized industry reports, then categorized and analyzed. The findings indicate that in the pre-production phase, screenwriting tools, storyboard generation, and intelligent pre-visualization play an increasingly prominent role; in the production phase, image stabilization systems, smart drones, and digital facial reconstruction are gaining ground; and in the post-production phase, automated editing, deepfake technology, and AI-driven color correction are becoming indispensable to filmmakers' workflows. The study further reveals that alongside such advantages as reduced production time and cost and greater access for independent filmmakers to professional-grade tools, the application of artificial intelligence in cinema is accompanied by serious concerns—among them a crisis of trust in the image stemming from deepfake technology, unresolved legal and intellectual-property questions, and a potential erosion of artistic authenticity. The findings of this study may offer film scholars, filmmakers, and cultural policymakers a more precise understanding of this technology and a foundation for its more conscientious application.
Downloads
References
Agrahari Baniya, A., Lee, T. K., Eklund, P. W., & Aryal, S. (2025). A survey of deep learning video super-resolution. arXiv. https://arxiv.org/abs/2506.03216
Bernabo, L. (2025). How, when, and why to use AI: Strategic uses of professional perceptions and industry lore in the dubbing industry. International Journal of Communication, 19, 698–715.
Bharathi, V. J., & Vardhan, V. V. (2024). Will AI replace human jobs in the film production? ShodhKosh: Journal of Visual and Performing Arts, 5(ICITAICT), 33–39.
Davaasambuu, T. (2025). Film production artificial intelligence use. Art and Design Review, 13(3), 210–220.
Deckker, D., & Sumanasekara, S. (2025). A review of AI-powered creativity: The intersection of AI and the arts. International Journal of Global Economic Light (JGEL), 11(4), 10–24.
Estrada Torrejon, O., Peretti, N., & Figueroa, R. (2020). Rotoscope automation with deep learning. SMPTE Motion Imaging Journal, 129(2), 16–26.
Hossain, K., & Deb, J. (2025). A case study on integrating AI in making an animation movie. International Journal for Multidisciplinary Research (IJFMR), 7(2), 1–7.
Jia, J., & Chen, J. (2026). A study of music composition models combining generative adversarial networks and their application to film scoring. International Journal of Computer Information Systems and Industrial Management Applications, 18, 1–12.
Kadner, N. (2019). The virtual production field guide (Vol. 1). Epic Games.
Koskinen, M. (2015). Color grading a feature film for digital cinema. Tampere University of Applied Sciences.
Loock, K. (2021). On the realist aesthetics of digital de-aging in contemporary Hollywood cinema. Orbis Litterarum, 76, 214–225.
Martínez-Ramírez, M. A., Liao, W. H., Fabbro, G., Uhlich, S., Nagashima, C., & Mitsufuji, Y. (2022). Automatic music mixing with deep learning and out-of-domain data. Proceedings of the 23rd International Society for Music Information Retrieval Conference, 1–8.
Nan, S., Li, M., Zheng, S., Lu, Y., Zhang, H., & Fu, Y. (2026). Mind-of-Director: Multi-modal agent-driven film previsualization via collaborative decision-making. arXiv. https://arxiv.org/abs/2603.14790
Osemwegie, O. (2025). Neural rendering and deep learning in virtual production: Redefining cinematic storytelling, CGI, and real-time content generation. International Journal of Mechanical and Thermal Engineering, 6(1), 40–52.
P, A., & Nirmala, T. (2024). An analysis of artificial intelligence techniques on animation in animated movie series. ShodhKosh: Journal of Visual and Performing Arts, 5(ICITAICT), 19–25.
Qiu, S. (2023). Generative AI processes for 2D platformer game character design and animation. Proceedings of the 2nd International Conference on Interdisciplinary Humanities and Communication Studies, 146–160.
Reid, D. R., & Sanders, B. (2021). Documentary making for digital humanists. Open Book Publishers.
Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
Shaker, Z. T. (2025). Artificial intelligence and its applications in film and television editing. International Journal of Humanities and Arts, 7(2), 121–126.
Totlani, K. (2023). The evolution of generative AI: Implications for the media and film industry. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 11(10), 973–980.
Warankar, M., & Patil, R. (2024). Generative artificial intelligence. International Journal of Scientific Research in Engineering and Management, 8(4), 1–4.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Khudai Nazar Musafer, Faridullah Farahmand

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















