Call for Papers

The research session is dedicated to studies that investigate Artificial Intelligence, AI research, or a clearly defined AI-related topic through the scientific literature.

We welcome contributions using approaches such as:

• Scientometric and bibliometric analyses of AI research;
• Systematic literature reviews, structured literature reviews, scoping reviews, and mapping studies;
• Thematic or conceptual analyses of scientific literature;
• Science mapping, research-front, topic evolution, and thematic development analyses;
• Citation, co-citation, bibliographic coupling, co-authorship, and scientific collaboration analyses;
• Institutional, geographic, disciplinary, or interdisciplinary analyses of AI research;
• Methodological studies concerning the design, reproducibility, or evaluation of AI-related literature analyses;
• Studies examining the use of AI or GenAI within research practice, scholarly communication, or literature-analysis workflows.

What Is Outside the Scope?

The research session does not accept:

• General Machine Learning experiments without a literature-analysis component;
• Software demonstrations;
• Model benchmarking studies without a literature-analysis component;
• Opinion papers;
• Purely narrative overviews without a clearly described research method.

Submissions should take the form of an extended research note of up to 6 pages, including references.

Initial submissions are accepted in free format. No workshop template is required for peer review, provided that the manuscript is clear, readable, and organized as an academic research note or short paper.

Manuscripts must be submitted as PDF files.

Completed and Ongoing Research

Both completed and ongoing research are eligible.

Submissions may report final, intermediate, preliminary, or partial results from a clearly defined stage of a larger research project. Intermediate findings do not need to be presented as final conclusions.

Authors should clearly distinguish what has already been established from analyses or work still in progress.

Suggested Content

Because the workshop accepts different forms of scientometric and literature-based research, authors are not required to follow a single section structure.

Depending on the type and stage of the study, submissions should normally report:

• Research question, objective, or clearly defined analytical problem;
• AI-related topic and rationale for the study;
• Databases, scholarly sources, or corpus used;
• Date or period of data collection;
• Search strategy, search query, or literature-identification procedure;
• Inclusion and exclusion criteria, where applicable;
• Data cleaning, screening, and deduplication procedures, where applicable;
• Analytical methods, indicators, and software used;
• Results, intermediate findings, or preliminary patterns currently available;
• Interpretation of the findings at the current stage of the research;
• Limitations and next analytical steps, particularly for ongoing studies;
• Contribution of the study;
• Data and code availability statement, where applicable;
• Declaration of the use of Generative AI tools, where applicable.

ARC AWARE encourages transparent and reproducible literature-based research. Where licensing conditions permit, authors are encouraged to make available the analytical code, workflow, derived data, and supporting materials necessary to understand or reproduce the analysis.

Supporting materials may be deposited in Zenodo or another established research repository.

Raw bibliographic records should only be redistributed where permitted by the terms and conditions of the source database. Where redistribution is restricted, authors should provide the search strategy, processing code, documentation, and other materials that allow the analytical workflow to be reconstructed as closely as possible.

Each extended research note will undergo double-blind peer review by two reviewers. Authors must therefore submit an anonymized manuscript for review.

Submissions will be evaluated according to:

• Fit with the scope of Scientometric and Literature-Based Perspectives on Artificial Intelligence;
• Clarity of the research question or objective;
• Appropriateness and transparency of the methodological design;
• Quality and transparency of the literature identification, data collection, screening, or corpus-construction process;
• Adequacy of the analysis for the stated research question;
• Clarity with which the current findings and stage of the research are reported;
• Correspondence between the results and the claims made by the authors;
• Clarity and concision of presentation.

Approximately 6–10 contributions will be selected for presentation.

Acceptance for presentation does not automatically imply final acceptance for publication. Authors may be asked to revise their research note following reviewer comments.