When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge

2026-05-07

AI Summary

Artificial intelligence is profoundly reshaping scientific research, yet its implications for diversity, reproducibility, and ethics remain inadequately understood. This study presents the first systematic empirical analysis of the global landscape and associated risks of AI-driven research, drawing on bibliometric data from scientific publications worldwide between 1960 and 2023. Integrating large-scale bibliometric analysis, a cross-disciplinary classification framework, and time-series modeling, the work reveals that AI-related outputs are heavily concentrated in computer science, exhibit a significant citation premium, and are associated with higher retraction rates. Concurrently, middle-income countries in Asia demonstrate rapidly growing research influence, underscoring pronounced geographical heterogeneity. These findings provide an empirical foundation for understanding the paradigm shift induced by AI in scientific inquiry and its attendant challenges.

Abstract

The extent to which Artificial Intelligence (AI) can trigger generalized paradigm shifts in science is unclear. Although some of these technologies have revolutionized data collection and analysis in specific scientific fields such as Chemistry, their overall impact depends on the scope of adoption and the ways scholars use them. In this study, we document substantial differences in the timing and extent of AI adoption across countries and scientific domains from 1960 to 2015. After 2015, we find generalized exponential growth in AI adoption, with the number of AI-supported works multiplying by at least four across all domains. The transformative nature of this rapid growth is less apparent and points to multiple challenges should adoption trends persist. According to our analyses, AI-supported research is confined to very few topics with strong ties to Computer Science and conventional statistical frameworks, suggesting limited transformational potential in epistemological terms. AI-supported works are also associated with an unwarranted citation premium and exhibit substantially higher retraction rates than non-AI-supported works across most fields. Geographically, AI adoption displays pronounced heterogeneity at the country level, along with an acceleration in the relevance of middle-income countries in Asia, from China and beyond. Thus, the transformative capacity of AI in science remains largely untapped, and its rapid adoption underlines challenges in research openness, transparency, reproducibility, and ethics from a global perspective. We discuss how best research practices could boost the benefits of AI adoption and highlight fields and geographies where these trends warrant closer scrutiny.

Problem

• Artificial Intelligence
• research transformation
• interdisciplinarity
• retraction rates
• global research inequality

Innovation

• AI adoption
• research reproducibility
• citation bias
• scientific retraction
• interdisciplinary research