Generative AI lacks the human creativity to achieve scientific discovery from scratch

Amy Wenxuan Ding () and Shibo Li
Additional contact information
Amy Wenxuan Ding: EM - EMLyon Business School
Shibo Li: Indiana University [Bloomington] - Indiana University System

Post-Print from HAL

Abstract: Scientists are interested in whether generative artificial intelligence (GenAI) can make scientific discoveries similar to those of humans. However, the results are mixed. Here, we examine whether, how and what scientific discovery GenAI can make in terms of the origin of hypotheses and experimental design through the interpretation of results. With the help of a computer-supported molecular genetic laboratory, GenAI assumes the role of a scientist tasked with investigating a Nobel-worthy scientific discovery in the molecular genetics field. We find that current GenAI can make only incremental discoveries but cannot achieve fundamental discoveries from scratch as humans can. Regarding the origin of the hypothesis, it is unable to generate truly original hypotheses and is incapable of having an epiphany to detect anomalies in experimental results. Therefore, current GenAI is good only at discovery tasks involving either a known representation of the domain knowledge or access to the human scientists' knowledge space. Furthermore, it has the illusion of making a completely successful discovery with overconfidence. We discuss approaches to address the limitations of current GenAI and its ethical concerns and biases in scientific discovery. This research provides insight into the role of GenAI in scientific discovery and general scientific innovation.

Keywords: Scientific discovery; Generative artificial intelligence; Large Language models; ChatGPT (search for similar items in EconPapers)
Date: 2025-03-20
New Economics Papers: this item is included in nep-exp and nep-sbm
Note: View the original document on HAL open archive server: https://hal.science/hal-05053017v1
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Scientific Reports, 2025, 15, 12 p. ⟨10.1038/s41598-025-93794-9⟩

Downloads: (external link)
https://hal.science/hal-05053017v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05053017

DOI: 10.1038/s41598-025-93794-9

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().