“Who” Is the Best Creative Thinking Partner? An Experimental Investigation of Human–Human, Human–Internet, and Human–AI Co‐Creation

The Journal of Creative Behavior, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

ABSTRACT Recent research suggests that working with generative artificial intelligence (AI), such as ChatGPT, can produce more creative outcomes than humans alone. However, does AI retain its edge when have access to alternative information sources, another human or the internet. We explored this question in a between‐group experiment 202 German participants across four conditions (human–human dyads, human–Internet, and two human–AI groups basic specific instructions) creativity tasks (two alternate uses tasks, consequences task, problem‐solving task). Results showed human–human condition obtained higher scores divergent thinking remaining groups. No significant differences were observed task. Moreover, interacting dyads made people creatively confident, an effect not other In addition, we compared human‐rated AI‐based automated scoring (Ocsai). Interestingly, notable discrepancies emerged between assessment human‐judged results, raising concerns about AI's susceptibility “elaboration bias.” These findings highlight benefits of collaboration for call further studies reliability potential biases evaluating performance.

Language: Английский

An Exploration of Machine Learning in Art and Design

Advances in human and social aspects of technology book series, Journal Year: 2024, Volume and Issue: unknown, P. 205 - 236

Published: Oct. 4, 2024

The paper delves into the profound impact of Artificial Intelligence (AI) on various creative domains, emphasizing its transformative potential in reshaping human-computer interaction. It explores AI's evolution from traditional programming to Machine Learning (ML), highlighting ML's pivotal role facilitating tasks traditionally reserved for human intelligence. text elucidates how ML algorithms, such as Generative Adversarial Networks (GANs) and Neural Style Transfer, have revolutionized artistic creation by enabling computers generate visually captivating artworks autonomously. Furthermore, it discusses democratizing effect AI art design, making tools techniques more accessible individuals diverse backgrounds. While acknowledging ethical implications challenges, bias interpretability concerns, advocates interdisciplinary collaboration address these issues responsibly.

Language: Английский