Rise of Generative Artificial Intelligence in Science

2024-12-30

AI Summary

This study investigates the evolutionary dynamics of generative artificial intelligence (GenAI) in scholarly publishing from 2017 to 2023, using OpenAlex bibliometric data. Methodologically, it employs scientometric analysis, time-series modeling, and cross-regional/cross-disciplinary comparative statistics to examine disciplinary diffusion, geographic distribution, and research collaboration patterns. Key contributions include: (1) empirical evidence that GenAI research teams are significantly smaller than those in traditional AI, yet exhibit comparable international co-authorship rates—challenging the assumption that smaller teams imply lower collaborative intensity; (2) documentation of rapid interdisciplinary expansion beyond computer science; and (3) quantification of national contributions, revealing the U.S. accounts for nearly 40% of global GenAI publications, China ranks second, and several small-to-medium advanced economies demonstrate notable activity. The findings provide empirically grounded, structurally articulated insights into GenAI-driven transformations in scientific practice and knowledge production.

Technology Category

• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Application Domains: Humanities & Computational Social Science
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining

Abstract

Generative Artificial Intelligence (GenAI, generative AI) has rapidly become available as a tool in scientific research. To explore the use of generative AI in science, we conduct an empirical analysis using OpenAlex. Analyzing GenAI publications and other AI publications from 2017 to 2023, we profile growth patterns, the diffusion of GenAI publications across fields of study, and the geographical spread of scientific research on generative AI. We also investigate team size and international collaborations to explore whether GenAI, as an emerging scientific research area, shows different collaboration patterns compared to other AI technologies. The results indicate that generative AI has experienced rapid growth and increasing presence in scientific publications. The use of GenAI now extends beyond computer science to other scientific research domains. Over the study period, U.S. researchers contributed nearly two-fifths of global GenAI publications. The U.S. is followed by China, with several small and medium-sized advanced economies demonstrating relatively high levels of GenAI deployment in their research publications. Although scientific research overall is becoming increasingly specialized and collaborative, our results suggest that GenAI research groups tend to have slightly smaller team sizes than found in other AI fields. Furthermore, notwithstanding recent geopolitical tensions, GenAI research continues to exhibit levels of international collaboration comparable to other AI technologies.

Problem

• Generative Artificial Intelligence
• Scientific Research
• International Collaboration

Innovation

• OpenAlex
• Generative AI
• Trend Analysis