Paper Type

Complete

Paper Number

1770

Description

The work investigates whether AI catalyzes scientific creativity and explores the theoretical explanations behind it. Employing the Logical Creative Thinking (LCT) framework, we conjecture that AI enhances scientific creativity by providing faster search algorithms and offering possibilities to explore new search paths for uncovered knowledge. AI is expected to facilitate creative knowledge hybridization (i.e., recombination in LCT) across fields and serve as a stimulus for knowledge mutation (i.e., replacement in LCT) within a field. We consider two measures of scientific creativity: novelty (as hybridization) and disruption (as mutation). We analyze publications from 2000 to 2021 and their citation networks. Our findings first inform that AI increases the novelty of mediocre (medium-level) and the top (90th-percentile) papers while enhancing the disruption of the mediocre papers only. Second, we identify nuanced variations in the impact on creativity across fields. Third, our citation-network analyses further uncover the direct and indirect effects of AI.

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Jul 2nd, 12:00 AM

The Philosopher’s Stone for Science – The Catalyst Change of AI for Scientific Creativity

The work investigates whether AI catalyzes scientific creativity and explores the theoretical explanations behind it. Employing the Logical Creative Thinking (LCT) framework, we conjecture that AI enhances scientific creativity by providing faster search algorithms and offering possibilities to explore new search paths for uncovered knowledge. AI is expected to facilitate creative knowledge hybridization (i.e., recombination in LCT) across fields and serve as a stimulus for knowledge mutation (i.e., replacement in LCT) within a field. We consider two measures of scientific creativity: novelty (as hybridization) and disruption (as mutation). We analyze publications from 2000 to 2021 and their citation networks. Our findings first inform that AI increases the novelty of mediocre (medium-level) and the top (90th-percentile) papers while enhancing the disruption of the mediocre papers only. Second, we identify nuanced variations in the impact on creativity across fields. Third, our citation-network analyses further uncover the direct and indirect effects of AI.

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