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Exploring the Collaborative Co-Creation Process with AI: A Case Study in Novice Music Production

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arxiv 2501.15276 v2 pith:C5K3I7ES submitted 2025-01-25 cs.HC cs.AI

classification cs.HCcs.AI
keywords co-creationcreativemusicstagecasecollaborativegrouphuman-ai
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Artificial intelligence is reshaping creative domains, yet its co-creative processes, especially in group settings with novice users, remain under explored. To bridge this gap, we conducted a case study in a college-level course where nine undergraduate students were tasked with creating three original music tracks using AI tools over 10 weeks. The study spanned the entire creative journey from ideation to releasing these songs on Spotify. Participants leveraged AI for music and lyric production, cover art, and distribution. Our findings highlight how AI transforms creative workflows: accelerating ideation but compressing the traditional preparation stage, and requiring novices to navigate a challenging idea selection and validation phase. We also identified a new "collaging and refinement" stage, where participants creatively combined diverse AI-generated outputs into cohesive works. Furthermore, AI influenced group social dynamics and role division among human creators. Based on these insights, we propose the Human-AI Co-Creation Stage Model and the Human-AI Agency Model, offering new perspectives on collaborative co-creation with AI.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing

    cs.SD 2025-07 conditional novelty 7.0 of 10

    MixAssist is the first audio-grounded, multi-turn conversational dataset for co-creative music mixing instruction, and fine-tuning Qwen-Audio on it yields human-comparable mixing advice.

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