The implications of generative AI for creative fields span across visual arts, design, film, television, performing arts, writing, and media industries, with potential affordances and concerns in both the process of creation and the industries at large. As in other fields, generative AI can automate repetitive tasks and provide efficiencies, access, and a speeding up of processes that allow for new possibilities in art, design, performance, and across media. However, the very concept of AI-generated art challenges current creation models, raises questions around authorship, authenticity, and ownership of creative works, is leading to reimagining how we define originality, and may put employees in creative industries, including creators, at significant risk of job loss and replacement. Generative AI raises new copyright and attribution issues, with the creative industry, courts, and regulators still navigating how it will affect the future of copyright and attribution (Holloway, Cheng, and Dickenson, 2024) (Appel, Neelbauer, and Schweidel, 2023).

Creative AI tools are proliferating that will facilitate every imaginable aspect of creative and composition processes, whether this be in visual arts, design, writing, music composition, filmmaking, video, or other fields. These delve into the most minute aspect of complex creative and technical tasks in ways that were once only available through human collaborators – and in some ways, not previously possible. Tasks such as proof-reading, editing, creating drafts and proofs-of-concept, iterative refinement and the integration of new works, information and tools are made possible within minutes.

In the visual arts and design, generative AI is seen as a powerful tool that can automate repetitive tasks, such as resizing and cropping images, while ensuring design consistency and quality. It can accelerate conceptualization, prototyping, and design verification, making these processes faster and more cost-effective. This technology has also led to collaborations between AI and artists, blending human creativity with AI’s capabilities to produce novel artworks that challenge traditional perceptions of art and creativity. For example, AI has been used to generate “Dali-like” images, which were then turned into three-dimensional objects through 3D printing and casting in bronze, highlighting the blend of AI capabilities and human craftsmanship (Shulman, 2024). AI can be applied to architectural design to address challenges and conceive innovative solutions. By inputting specific parameters such as materials, site conditions, and budget constraints, generative AI can quickly offer multiple design options that meet those requirements. This not only accelerates the design process but also opens up new avenues for creativity and collaboration. Despite its potential, challenges such as ensuring the feasibility and aesthetic quality of generated designs remain (Hakimshafaei, 2023, Houhou, 2023).

In film, television, and media production, generative AI promises to streamline video production through automated transcripts, video tagging, predictive editing, and real-time feedback, potentially enhancing efficiency in post-production and cross-platform optimization. In writing and literature, generative AI tools like language models have started assisting with content creation, offering new ways to brainstorm ideas, draft stories, and even generate entire narratives. Emerging applications of AI in music composition and the music industry are rapidly transforming how music is created, produced, and consumed. AI-powered tools are enhancing efficiency in audio processing tasks such as drum track alignment, vocal tuning, noise reduction, and audio quality enhancement during mixing and mastering stages. Online services like LANDR utilize AI algorithms for audio track analysis and adjustments, providing affordable and high-quality mastering services to artists without extensive audio engineering knowledge. The integration of AI in music production has also led to the development of virtual instruments and vocal synthesizers capable of producing realistic sounds (Steen and Lux, 2024). Generative AI applications in music composition are expanding into areas like media production, interactive music experiences, remixing, music production, and sound design. These applications are fostering new forms of creative expression and enabling composers to explore novel musical ideas with the support of AI (STL Digital, 2024).

Copyright, Intellectual Property and Applications in Creative Industries

Recent union negotiations involving authors, actors, and film workers have placed a significant emphasis on the use of artificial intelligence (AI) in their industries. These discussions have centered around concerns related to job security, copyright, and the ethical use of AI in creative work. The Writers Guild of America (WGA) successfully concluded negotiations with Hollywood Studios, addressing the use of AI in the writing process. The agreement ensures that AI cannot be used to write or rewrite scripts and that AI-generated writing will not be considered source material. Moreover, individual writers retain the choice to use AI tools, but companies cannot mandate their use (WGA 2023). SAG-AFTRA, the union representing film and TV performers, announced a deal with Replica Studios concerning the use of AI in voice acting, specifically in video games. This agreement establishes protections for digitally replicated voices, requiring consent from performers before their voices can be used and allowing them to opt out of continuous use in future projects (SAG-AFTRA 2024). The Author’s Guild has been very aggressive about pursuing the rights of authors in relation to the use of generative AI to train, reproduce or imitate authored works (The Authors Guild Bulletin, 2023). The Guild provides a guidance page on AI Best Practices for authors. They include the following suggestions for using generative AI ethically:

1. Use AI as an assistant for brainstorming, editing, and refining ideas rather than a primary source of work, with the goal of maintaining the unique spirit that defines human creativity. Use AI to support, not replace, this process.
2. To the extent you use AI to generate text, be sure to rewrite it in your own voice before adopting it. If you are claiming authorship, then you should be the author of your work.
3. If an appreciable amount of AI-generated text, characters, or plot are incorporated in your manuscript, you must disclose it to your publisher and should also disclose it to the reader. We don’t think it is necessary for authors to disclose generative AI use when it is employed merely as a tool for brainstorming, idea generation, or for copyediting.
4. Respect the rights of other writers when using generative AI technologies, including copyrights, trademarks, and other rights, and do not use generative AI to copy or mimic the unique styles, voices, or other distinctive attributes of other writers’ works in ways that harm the works. (Note: doing so could also be subject to claims of unfair competition).
5. Thoroughly review and fact-check all content generated by AI systems. As of now, you cannot trust the accuracy of any factual information provided by generative AI. All Chatbots now available make information up. They are text-completion tools, not information tools. Also, be aware and check for potential biases in the AI output, be they gender, racial, socioeconomic, or other biases that could perpetuate harmful stereotypes or misinformation.
6. Show solidarity with and support professional creators in other fields, including voice actors and narrators, translators, illustrators, etc., as they also need to protect their professions from generative AI uses. (The Authors Guild, 2024)

Generative AI in Student Creative Works

Generative AI tools can serve to foster creativity, innovation, and efficiency in students’ projects; yet they also necessitate a mindful approach to their integration into educational practices. In design and arts, for example, AI-generated prototypes can serve as starting points for discussions about aesthetic, functional, and technical considerations. Similarly, in writing and music, AI-generated samples can illustrate how different styles or elements might alter the perception of a piece, thereby deepening students’ understanding of their craft. By integrating generative AI into student work, students will be prepared for a future in which these tools will likely play a significant role in creative industries. Familiarity with AI tools and an understanding of their potential and limitations will equip students with the skills necessary to navigate the future job market and contribute to the evolution of their fields. While generative AI can automate certain aspects of the creative process, we should emphasize the irreplaceable value of human creativity, judgment, and ethical considerations.

Encouraging students to use AI as a tool to augment their work rather than replace the creative process can help maintain this balance. This approach ensures that AI serves to enhance student learning and creativity while acquiring essential skills, including AI fluency in their field. Faculty will need to establish clear guidelines on the ethical use of generative AI in student creative work. This includes understanding copyright and attribution, emphasizing the importance of originality, and encouraging students to critically engage with AI-generated content as part of their creative process, not as the endpoint. Discussions around the potential biases inherent in AI models and the importance of critical, human oversight in the creative process are also crucial.

Summary and Additional Considerations

The use of AI and generative models has the ability to greatly propel research and discovery. How and where the models are used in the research process will likely be both systematic and individualized across and within disciplines. Thus, there are expected to be commonalities and great variabilities in how research has been traditionally done and how AI is going to change the process. Identifying where it can be particularly useful and where it might be less useful will likely be  a research domain in and of itself, as well as how it impacts the student research growth process.

With the now massive amounts of data and information available, the need for sophisticated tools to help parse and analyze the information is paramount. Nonetheless, insights from humans will remain an integral part of the scientific research process. As described in a recent publication on AI and understanding in scientific research:

 “Training the next generation of scientists to identify and avoid the epistemic risks of AI will require not only technical education, but also exposure to scholarship in science and technology studies, social epistemology and philosophy of science” (Messeri & Crockett, 2024).

Additional topics that are expected to be relevant for future consideration of AI in the research process are leveraging AI to facilitate automated tasks in the research workflow (including administrative tasks like using AI to administer sponsored research), time management and in particular automated tasks, as well as complementing strengths and weaknesses for researchers in the research workflow. Each of these will require assessment of pros and cons for a given field or across disciplines, including preserving intellectual property for research still in its infancy or unpublished state.