Conference Paper/Proceeding/Abstract 252 views 16 downloads
Battle Rap as a Framework for Human-Machine Co-Creation
Ibukun Olatunji,
Matt Sheppard,
Matt Jones 
,
Alma Rahat,
Amanda Rogers
,
Alma Rahat,
Amanda Rogers
ICCC 2025 Proceedings of the Sixteenth International Conference on Computational Creativity
Swansea University Authors:
Matt Jones , Alma Rahat, Amanda Rogers
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Abstract
We present a human-in-the-loop GAN framework for battle rap, where a human artist (MC) serves as generator, and the AI acts as an adaptive discriminator. The AI provides feedback on rhyme complexity, coherence, and stylistic alignment, challenging the MC’s improvisational skill. Fine-tuned language...
| Published in: | ICCC 2025 Proceedings of the Sixteenth International Conference on Computational Creativity |
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| ISBN: | 978-989-54160-7-3 |
| ISSN: | 3051-6706 |
| Published: |
State University of Campinas (Unicamp) Brazil
Association for Computational Creativity (ACC)
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71550 |
| Abstract: |
We present a human-in-the-loop GAN framework for battle rap, where a human artist (MC) serves as generator, and the AI acts as an adaptive discriminator. The AI provides feedback on rhyme complexity, coherence, and stylistic alignment, challenging the MC’s improvisational skill. Fine-tuned language models emulate diverse rap styles, while voice cloning creates adversarial loops: the MC competesagainst stylised versions of their own voice in a dynamic, selfreflective duel. The system follows a dual-phase process: (i) an Emulation Phase, where AI mimics established flows to reinforce technical mastery, and (ii) an Improvisation Phase, where AI disrupts expectations to prompt originality. This ensures that creative growth emerges from constraint and challenge. Success is judged through MC evaluations of the AI’s performance as an adversary. Framed as a study paper, this work offers a thought experiment in adversarial co-creativity, modelling how AI might inspire, rather than merely assist, human expression. Beyond computational modelling, the framework offers insights into machine-mediated creativityand how AI can be designed to provoke human creativity through improvisation, challenge, and real-time performance. The study positions the AI as a dynamic co-performer capable of eliciting novel artistic responses. As such, it contributes to emerging discourse on creative AI systems that influence, not just assist, human expression |
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| College: |
Faculty of Science and Engineering |
| Funders: |
EPSRC studentship |