Expanding the HAISP Dataset: AI's Impact on Songwriting Across Two AI Song Contests

As artificial intelligence (AI) continues to shape creative practices, understanding its role in human-AI songwriting remains crucial. This paper expands the Human-AI Songwriting Processes (HAISP) dataset by incorporating data from the 2024 AI Song Contest, building upon the original 2023 dataset. By analyzing new submissions, we provide further insights into AI's evolving impact on songwriting workflows, creative decision-making, and control. A comparative study of AI tool usage and participant strategies between the 2023 and 2024 contests reveals shifts in collaboration patterns and tool effectiveness. Additionally, we assess the differences between general-purpose AI systems and personalized, fine-tuned tools, highlighting their impact on creative agency. Our findings offer key design implications for AI-assisted songwriting tools, providing actionable insights for AI developers and music practitioners seeking to enhance co-creative experiences.

Publication Details

Journal
Fraunhofer-Publica (Fraunhofer-Gesellschaft)
Published
2025-09-21
DOI
https://doi.org/10.5281/zenodo.17706323
Primary Topic
Music Technology and Sound Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Expanding the HAISP Dataset: AI's Impact on Songwriting Across Two AI Song Contests

Fraunhofer-Publica (Fraunhofer-Gesellschaft)
Music Technology and Sound Studies
article

Expanding the HAISP Dataset: AI's Impact on Songwriting Across Two AI Song Contests

article en

Abstract

As artificial intelligence (AI) continues to shape creative practices, understanding its role in human-AI songwriting remains crucial. This paper expands the Human-AI Songwriting Processes (HAISP) dataset by incorporating data from the 2024 AI Song Contest, building upon the original 2023 dataset. By analyzing new submissions, we provide further insights into AI's evolving impact on songwriting workflows, creative decision-making, and control. A comparative study of AI tool usage and participant strategies between the 2023 and 2024 contests reveals shifts in collaboration patterns and tool effectiveness. Additionally, we assess the differences between general-purpose AI systems and personalized, fine-tuned tools, highlighting their impact on creative agency. Our findings offer key design implications for AI-assisted songwriting tools, providing actionable insights for AI developers and music practitioners seeking to enhance co-creative experiences.

Fraunhofer-Publica (Fraunhofer-Gesellschaft)
Peace, Justice and strong institutions
Openalex Percentile: Top 41%
Music Technology and Sound Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Expanding the HAISP Dataset: AI's Impact on Songwriting Across Two AI Song Contests · Fraunhofer-Publica (Fraunhofer-Gesellschaft) (2025) | TGRS Research Map | TGRS