AI4Science: New Paradigms and Trends

Artificial Intelligence for Science, commonly abbreviated as AI4Science, denotes the integration of AI technologies to enhance and accelerate scientific research. It encompasses the deployment of machine learning, neural networks, data analytics, and other AI tools to process extensive datasets, identify patterns, make predictions, and derive insights that would be challenging or impossible for humans using conventional methodologies. AI4Science enables more efficient data collection, hypothesis testing, experiment design, and the discovery of novel scientific knowledge.

By automating repetitive or time-consuming tasks, AI4Science allows researchers to focus on more creative and analytical aspects of their work, accelerating scientific discovery and innovation. AI4Science represents a paradigm shift in how scientific research is conducted, promising to revolutionize our understanding of the world and the development of new technologies.

This Research Topic, featured in the Scholarly Communication section of Frontiers in Research Metrics and Analytics, seeks to explore the transformative effects of AI tools on scientific inquiry, from enhancing data collection and hypothesis testing to redefining the entire lifecycle of research—from conception and experimentation to publication. Its goal is to uncover how AI is not merely a supportive technology, but a revolutionary force that is redefining the landscape of academic research.

Specific topics for submission include, but are not limited to, the following:

• the role of AI in reshaping the academic research landscape
• an overview of the state-of-the-art and trends in AI applications across different fields, including future directions
• an examination of AI’s impact on the scientific process, detailing how it modifies traditional research workflows, including conceptualization, methodology, data processing, investigation, writing, peer review, and publication
• insights into the application of AI in scientometrics and bibliometrics, including metrics research, evaluative research, citation context analysis, sentiment analysis, full-text content analysis, AI tools, etc.
• AI applications in LIS, including information retrieval and access, information systems, academic GPTs, etc.
• policy frameworks necessary for ensuring ethical and responsible AI4Science research practices
• the contribution of large language models (LLMs) to scientific research, analyzing their influence on literature reviews, hypothesis generation, experiment design, data analysis, interpretation, and research communication
• case studies exemplifying AI’s contribution to significant breakthroughs in the scientific community.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: AI4Science, AI, scientific research, transformative power of AI, discovery, innovation

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.