Unpacking How GenAI is Revolutionizing and Reshaping the Human Experience in Creative Work
Bibliographic record
Abstract
In recent years, the rollout of generative artificial intelligence (GenAI) for public use has sparked intense debates on the use of this tool for creativity purposes (Amabile, 2020). While some creative professionals have welcomed the use of such tools by creating new categories in creative competitions (ADC Awards, 2024), others have actively resisted GenAI based on concerns of their intellectual property being violated (Akers, 2024; Andersen, 2022). In response to this, an increasing number of studies have examined the use of GenAI in organizational creativity (Berg, Raj, & Seamans, 2023; Doshi & Hauser, 2024; Jia, Luo, Fang, & Liao, 2024). However, as the use of GenAI becomes an inevitable part of the creative process, we argue that the key question is no longer how GenAI affects the creative output, but rather how using GenAI fundamentally changes the way individuals navigate creative work. This change in focus prompts the need to bring the human experience back into the relationship between GenAI and creativity, and explore the situational factors affecting the human experience. This symposium hence aims to showcase the current research on the individual and contextual factors affecting GenAI and creativity. This symposium embarks on an insightful journey through four distinct yet interconnected research streams, delving into different experiences of when and how individuals navigate their creative work when using GenAI. Employing a diverse array of quantitative and qualitative methodologies at multiple levels of analysis, these studies reveal the contingencies involved when incorporating GenAI for creative work, and also mark a paradigmatic shift in our theoretical understanding of creative work. Generative AI and the Reallocation of Creative Effort Author: Nelberto Nicholas Marcos Quinto; University College London Author: Sarah Harvey; Effects of initial AI use and competition outcome on subsequent reliance on AI Author: Velvetina Siu Ching Lim; Author: Yamon Min Ye; Author: Tianyu He; National University of Singapore Creative Markets in the Age of Generative AI: Strategic Shifts and Labor Market Health Author: Eric Zhou; Boston University Author: Dokyun Lee; Author: Gordon Burtch; Boston University Author: Daniel Rock; University of Pennsylvania Author: Prasanna Tambe; Monsters of Our Own Creation: AI, Occupational Cannibalization, and the Future of Work Author: Kevin Woojin Lee; The University of British Columbia
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.056 |
| Scholarly communication | 0.024 | 0.030 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".