AI in Academic Metrics and Impact Analysis
A special issue of Publications (ISSN 2304-6775).
Deadline for manuscript submissions: 31 March 2026 | Viewed by 178
Special Issue Editors
Interests: complex network methods and applications; scientific prediction and evaluation; data mining
Special Issue Information
Dear Colleagues,
For centuries, experimental and theoretical sciences have served as the two foundational paradigms in the scientific community, but a new generation of artificial intelligence (AI) is now giving rise to a new paradigm of scientific research. Utilizing AI technologies to address scientific problems will greatly accelerate the efficiency of scientific investigation. This increase in research efficiency will, in turn, enhance AI capabilities, ultimately creating a new research paradigm driven by the resonant engine of the dual spiral of AI4Science and Science4AI. Imagine a future where scientific research involves the automatic generation and verification of hypotheses, the intelligent discovery of new laws, and the automated proposal of new principles—a whole new era in science. How the field of scientometrics can seize this wave of AI to build a more scientific and intelligent research evaluation system is an issue that forward-thinking scientometricians need to consider and address.
This Special Issue aims to create a platform for peers to engage in discussions, focusing on the cutting-edge advancements and key breakthroughs of AI in research evaluation. The application of AI promises to fundamentally change traditional academic assessment, citation analysis, and measures of research impact, significantly enhancing the accuracy, interpretability, and automation of evaluations. In the context of human–AI collaboration, AI can also serve as an intelligent assessment assistant, improving the understanding and application efficiency of academic achievements. This Special Issue particularly focuses on the following research topics: (1) AI exploration in identifying original research; (2) the automation of research result screening and recommendations based on AI; (3) the AI-based evaluation of high-value patents; (4) the impact and challenges of AI technology in scientometrics; (5) the trustworthiness of AI in research evaluation, among others.
Dr. Guoqiang Liang
Dr. Shuo Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- scientometric assessment
- AI4Science
- LLMs
- AI agents
- patent analysis
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