The Role of AI in Facilitating Interdisciplinary Collaboration: Evidence from AlphaFold

• URL: http://arxiv.org/abs/2508.13234v2
• Date: Mon, 27 Oct 2025 07:32:43 GMT
• Title: The Role of AI in Facilitating Interdisciplinary Collaboration: Evidence from AlphaFold
• Authors: Naixuan Zhao, Chunli Wei, Xinyan Zhang, Jiang Li,
• Abstract summary: This study examines how AI technologies influence interdisciplinary collaborative patterns.<n>By analyzing 1,247 AlphaFold-related papers and 7,700 authors from Scopus, we employ bibliometric analysis and causal inference.<n>We show that AlphaFold increased structural biology-computer science collaborations by just 0.48%, with no measurable effect on other disciplines.
• Score: 2.9501457365006476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The acceleration of artificial intelligence (AI) in science is recognized and many scholars have begun to explore its role in interdisciplinary collaboration. However, the mechanisms and extent of this impact are still unclear. This study, using AlphaFold's impact on structural biologists, examines how AI technologies influence interdisciplinary collaborative patterns. By analyzing 1,247 AlphaFold-related papers and 7,700 authors from Scopus, we employ bibliometric analysis and causal inference to compare interdisciplinary collaboration between AlphaFold adopters and non-adopters. Contrary to the widespread belief that AI facilitates interdisciplinary collaboration, our findings show that AlphaFold increased structural biology-computer science collaborations by just 0.48%, with no measurable effect on other disciplines. Specifically, AI creates interdisciplinary collaboration demands with specific disciplines due to its technical characteristics, but this demand is weakened by technological democratization and other factors. These findings demonstrate that artificial intelligence (AI) alone has limited efficacy in bridging disciplinary divides or fostering meaningful interdisciplinary collaboration.

Related papers

• Accelerating Scientific Research with Gemini: Case Studies and Common Techniques [105.15622072347811]
  Large language models (LLMs) have opened new avenues for accelerating scientific research.<n>We present a collection of case studies demonstrating how researchers have successfully collaborated with advanced AI models.
  arXiv  Detail & Related papers  (2026-02-03T18:56:17Z)
• Cyber Academia-Chemical Engineering (CA-ChemE): A Living Digital Town for Self-Directed Research Evolution and Emergent Scientific Discovery [3.8492432542613493]
  We present the Cyber Academia-Chemical Engineering (CA-ChemE) system.<n>By integrating domain-specific knowledge bases, knowledge enhancement technologies, and collaboration agents, the system successfully constructs an intelligent ecosystem.<n>This study demonstrates how carefully designed multi-agent architectures can provide a viable pathway toward autonomous scientific discovery in chemical engineering.
  arXiv  Detail & Related papers  (2025-10-01T05:26:55Z)
• The Incomplete Bridge: How AI Research (Mis)Engages with Psychology [30.36064725942852]
  Social sciences have accumulated a rich body of theories and methodologies for investigating the human mind and behaviors.<n> Focusing on psychology as a prominent case, this study explores the interdisciplinary synergy between AI and the field.<n>We identify key patterns of interdisciplinary integration, locate the psychology domains most frequently referenced, and highlight areas that remain underexplored.
  arXiv  Detail & Related papers  (2025-07-30T17:03:59Z)
• Scaling Laws in Scientific Discovery with AI and Robot Scientists [72.3420699173245]
  An autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle.<n>AGS aims to significantly reduce the time and resources needed for scientific discovery.<n>As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws.
  arXiv  Detail & Related papers  (2025-03-28T14:00:27Z)
• Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science [25.683422870223076]
  We present a large-scale analysis of the AI4Science literature.<n>We quantitatively highlight key disparities between AI methods and scientific problems.<n>We explore the potential and challenges of facilitating collaboration between AI and scientific communities.
  arXiv  Detail & Related papers  (2024-11-27T00:40:51Z)
• How Performance Pressure Influences AI-Assisted Decision Making [57.53469908423318]
  We show how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior.<n>Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Augmenting the Author: Exploring the Potential of AI Collaboration in Academic Writing [25.572926673827165]
  This case study highlights the importance of prompt design, output analysis, and recognizing the AI's limitations to ensure responsible and effective AI integration in scholarly work. The paper contributes to the field of Human-Computer Interaction by exploring effective prompt strategies and providing a comparative analysis of Gen AI models.
  arXiv  Detail & Related papers  (2024-04-23T19:06:39Z)
• Expanding the Role of Affective Phenomena in Multimodal Interaction Research [57.069159905961214]
  We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing. We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
  arXiv  Detail & Related papers  (2023-05-18T09:08:39Z)
• Questioning the impact of AI and interdisciplinarity in science: Lessons from COVID-19 [0.0]
  We show that scientific impact was not determined by the overall interdisciplinarity of author teams, but rather by the diversity of knowledge they actually harnessed. Our results provide insights into the ways in which team and knowledge structure may influence the successful integration of new computational technologies in the sciences.
  arXiv  Detail & Related papers  (2023-04-18T11:56:05Z)
• Quantifying the Benefit of Artificial Intelligence for Scientific Research [2.4700789675440524]
  We estimate both the direct use of AI and the potential benefit of AI in scientific research. We find that the use of AI in research is widespread throughout the sciences, growing especially rapidly since 2015. Our analysis reveals considerable potential for AI to benefit numerous scientific fields, yet a notable disconnect exists between AI education and its research applications.
  arXiv  Detail & Related papers  (2023-04-17T08:08:50Z)
• Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph [99.28342534985146]
  Existing literature and knowledge platforms on COVID-19 only focus on collecting papers on biology and medicine. We propose Covidia, COVID-19 interdisciplinary academic knowledge graph to bridge the gap between knowledge of COVID-19 on different domains.
  arXiv  Detail & Related papers  (2023-04-14T16:45:38Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• Extracting a Knowledge Base of Mechanisms from COVID-19 Papers [50.17242035034729]
  We pursue the construction of a knowledge base (KB) of mechanisms. We develop a broad, unified schema that strikes a balance between relevance and breadth. Experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature.
  arXiv  Detail & Related papers  (2020-10-08T07:54:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.