AI4SciSci 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.

Scholarly Graph Analytics
- Citation networks and their applications
- Social networks and their applications
- Knowledge graphs and their applications
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
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
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.