The 3^rd International Workshop on Artificial Intelligence for the Science of Science AI4SciSci Workshop 2026

About the 3^rd Workshop on AI4SciSci About AI4SciSci

Over the past decade, the field of artificial intelligence (AI) has witnessed significant advancements across various domains, such as natural language processing and computer vision, which have further accelerated discoveries, innovations, and breakthroughs in many interdisciplinary fields — including Science of Science, which relies on big data to uncover reproducible patterns governing the evolution of research topics, collaboration networks, knowledge diffusion, and the systemic factors that drive scientific progress. Today, Science of Science is incorporating AI techniques into the analysis of big data, yielding deep and compelling insights into both longstanding and emerging problems in the field. Building on the success of the first and second AI4SciSci workshops, held in conjunction with ICDM’23 and JCDL’25, respectively, we propose the Third International Workshop on Artificial Intelligence for the Science of Science (AI4SciSci) to further engage the AI and Science of Science communities in promoting research and discussion on frontier topics in this area.

Call for Papers

We welcome papers on topics of interest that include, but are not limited to, the following.

1. Scholarly Graph Analytics
   • Citation networks and their applications
   • Social networks and their applications
   • Knowledge graphs and their applications
2. Scholarly Data Mining
   • Innovation/novelty detection and prediction
   • Scholarly collaboration and mobility prediction
   • Career trajectory modeling and analysis
   • Numerical modeling of scientific development
   • Causal inference and reasoning for SciSci
   • Streaming data mining and its applications in SciSci
   • Analysis of large bibliographic datasets
   • Knowledge diffusion in scholarly networks
   • Causal inference in scholarly big data
3. Scientific Document Processing and Understanding
   • Scientific document parsing and its applications
   • Scientific metadata extraction and its applications
   • Scientific claim extraction and verification
   • Scientific tabular data structure recognition and understanding
   • Scientific figure and chart extraction and understanding
   • Scientific document summarization and topic modeling
4. Computational Social Sciences
   • Scientific reproducibility, replicability, repeatability, and generalizability
   • Automation of scientific innovation using AI
   • Societal benefits of AI in science and associated ethical implications
   • Socioeconomic effects on scientific progress and innovation
   • Scientific misinformation and disinformation
   • Predictive modeling of scientific impact
   • Scientific impact quantification using metrics beyond academia (e.g., patents, technology transfer, and industry revenue)
   • Science policy and its implications for research

Originality

One unique feature of our workshop is that we will accept both original and published submissions. Authors of published works will need to submit an extended abstract (up to 2 pages) that explicitly cites the published work. In addition to summarizing the published work, the extended abstracts may also contain incremental updates or new results of the published work.

Double-Blind Review and Anonymity Guidelines

Ai4SciSci adopts the same double-blind review policy as JCDL. Submissions must not include any information that could identify the authors, such as names, institutional affiliations, acknowledgments, or references to prior work written in the first person. Authors should refer to their own prior work in the third person (e.g., “Prior work by Smith et al. (2020)” rather than “In our previous work…”). If authors share supplementary materials (e.g., code, data, models), these must be anonymized to avoid revealing author identities. Authors are encouraged to use anonymous hosting platforms such as anonymous.4open.science.

Submissions

All submissions must be written in English, following the CEUR workshop proceedings style.

Paper submission link: EasyChair

Submission Length

• Extended Abstracts (up to 2 pages)
• Short Papers (up to 4 pages)
• Full Papers (up to 8 pages)

** References and appendices are not counted towards the page limits.

All accepted papers that report original work will be published at the CEUR proceedings.

Important Dates

• Paper Submission Deadline: September 7^th, 2026 Thursday, September 10^th, 2026
• Author Notification: September 21^st, 2026 Friday, October 02^nd, 2026
• Camera-Ready Deadline: October 5^th, 2026 Thursday, October 8^th, 2026
• Workshop date: Tuesday, October 13^th, 2026

Keynote Speaker

Dr. Yifan Qian

Research Assistant Professor

Northwestern University

---

Title: Science of Human–AI Coevolution

Machines have always been integral to scientific discovery, extending human reach and augmenting human ingenuity. Yet these machines largely remained instruments: humans formulated the questions, interpreted the evidence, evaluated explanations, and organized the collective enterprise of science. Large language models and AI agents are beginning to alter this division of cognitive labor, as machines move from passive instruments toward increasingly active epistemic participants. At the same time, our understanding of science remains overwhelmingly a story about humans. The Science of Science asks how science advances. AI for Science asks what machines can discover. But what happens to science—and to scientists—when machines become part of the system that advances it?

In this talk I will draw on our recent work on research funding and university innovation to illustrate the emerging dynamics of human–AI coevolution in science and innovation. In the funding pipeline, we study 5,700 confidential proposal submissions and 131,000 publicly released awards from the US National Science Foundation (NSF) and National Institutes of Health (NIH) in 2021–2025. We find that LLM use has risen sharply since 2023; proposals with higher LLM involvement closely resemble recently funded work at both agencies and are more likely to be funded and to yield early-stage publications at NIH but not at NSF, providing large-scale evidence that the rise of LLMs is reshaping how scientific ideas are positioned, selected, and translated into publicly funded research. In university innovation, using data that combines 15 universities' confidential faculty rosters, invention disclosures, patents, and licenses, we develop AI models to predict commercialization potential. These models reveal a large reservoir of faculty with high but unrealized innovation potential, and a randomized field experiment, partnering with the Technology Transfer Office at a major research university, shows that directing low-cost outreach to these faculty leads them to file more new invention disclosures than controls. Beyond documenting how AI is changing the way ideas get written, selected, and translated, this line of work offers measures and interventions with direct policy implications for funders, universities, and the design of human–AI partnerships..

Program Committee

Dr. Jian Wu

(Co-Chair)

Associate Professor

Old Dominion University, USA

jwu@cs.odu.edu

Dr. Sarah Rajtmajer

(Co-Chair)

Associate Professor

The Pennsylvania State University, USA

smr48@psu.edu

Dr. Yi He

(Co-Chair)

Assistant Professor

The College of William & Mary, USA

yihe@wm.edu

Dr. Chengzhi Zhang

Professor

Nanjing University of Science and Technology, China

Dr. Yi Zhao

Lecturer

Anhui University, China

Rochana R. Obadage

PhD Candidate

Old Dominion University, USA

Dr. Yang Yang

Assistant Professor

University of Notre Dame, USA

Dr. Daniel Acuna

Associate Professor

University of Colorado at Boulder, USA

Dr. Sai Koneru

Data Scientist

Pacific Northwest National Laboratory, USA

Dr. Hamed Alhoori

Associate Professor

Northern Illinois University, USA

Dr. Yifang Wang

Assistant Professor

Florida State University, USA

Erzhuo Shao

PhD Candidate

Northwestern University, USA

Dr. Tatiana Chakravorti

Postdoctoral Fellow

University of Alabama, USA

Dr. Giorgio Tripodi

Research Assistant Professor

Northwestern University, USA

Rui Pan

PhD Student

Northwestern University, USA

Zihang Lin

PhD Candidate

Northwestern University, USA

Dr. Diego Gómez-Zará

Assistant Professor

University of Notre Dame, USA