About Artificial Intelligence in Academic Research
Artificial Intelligence in Academic Research
About this Collection
This collection is now closed to submissions.
Academia is one of the key sectors driving the expansion of Artificial Intelligence (AI) and machine learning, with scholars from a variety of disciplines demanding, developing, and utilizing new applications for AI in their research. In fact, AI is becoming increasingly embedded in academic research as scholars turn to AI tools to find and analyze everything from datasets to published research articles, and from potential collaborators to job applicants. While the potential of these new technologies is exciting, the ramifications of using AI in academic research are only beginning to be explored. This collection welcomes original research on how AI techniques are used in (and on) academic research as well as critical reflections on these developments.
AI-powered tools, such as Semantic Scholar or Scite can help scholars at various stages of their research through contextualizing published content in a cohesive way. AI-powered research tools make it possible for scholars to easily access relevant repositories of information and analyse complex datasets easily. Other tools help publishers to identify potential peer reviewers, and journals to fight plagiarism. Finally, some tools explicitly recommend citations in a given context. These are just some examples of AI being applied to academic research in real-world applications, while AI techniques are still actively being researched. The time is ripe for assembling and consolidating ongoing streams of research, whether fundamental, applied or critical, into an open access collection.
This Collection aims to showcase research on AI techniques and methods applied to academic research, including their impact and critical assessment. Topics of interest include but are not limited to:
Submissions on new AI techniques and methods or novel applications of such techniques in academic research are welcome. We also invite reflections on methods related to studying AI in academic research. Empirical as well as theoretical work on the ethics and impact of AI in academic research is welcome.
Submissions are accepted for research articles, data, method and software tools articles, review articles, brief reports and opinion articles.
Keywords: AI, artificial intelligence, academic research, scholarly communication, scholarly publishing, bibliometrics, meta-research, scholarship, artificial intelligence, machine learning, AI fairness, AI ethics, research on research, science studies, research software, deep learning
Any questions about this collection? Please email research@f1000.com
This Collection is associated with the Research on Research, Policy & Culture and Artificial Intelligence and Machine Learning Gateways.
Academia is one of the key sectors driving the expansion of Artificial Intelligence (AI) and machine learning, with scholars from a variety of disciplines demanding, developing, and utilizing new applications for AI in their research. In fact, AI is becoming increasingly embedded in academic research as scholars turn to AI tools to find and analyze everything from datasets to published research articles, and from potential collaborators to job applicants. While the potential of these new technologies is exciting, the ramifications of using AI in academic research are only beginning to be explored. This collection welcomes original research on how AI techniques are used in (and on) academic research as well as critical reflections on these developments.
AI-powered tools, such as Semantic Scholar or Scite can help scholars at various stages of their research through contextualizing published content in a cohesive way. AI-powered research tools make it possible for scholars to easily access relevant repositories of information and analyse complex datasets easily. Other tools help publishers to identify potential peer reviewers, and journals to fight plagiarism. Finally, some tools explicitly recommend citations in a given context. These are just some examples of AI being applied to academic research in real-world applications, while AI techniques are still actively being researched. The time is ripe for assembling and consolidating ongoing streams of research, whether fundamental, applied or critical, into an open access collection.
This Collection aims to showcase research on AI techniques and methods applied to academic research, including their impact and critical assessment. Topics of interest include but are not limited to:
- AI-supported scholarly information retrieval
- AI-supported information extraction from academic research
- AI-supported plagiarism and scientific fraud detection
- AI and peer review
- AI and research evaluation
- AI and citations
- AI for summarizing literature
- AI for library collection management
- Ethics of using AI in academic research
- Fairness, accountability, transparency, and security of AI in academic research
Submissions on new AI techniques and methods or novel applications of such techniques in academic research are welcome. We also invite reflections on methods related to studying AI in academic research. Empirical as well as theoretical work on the ethics and impact of AI in academic research is welcome.
Submissions are accepted for research articles, data, method and software tools articles, review articles, brief reports and opinion articles.
Keywords: AI, artificial intelligence, academic research, scholarly communication, scholarly publishing, bibliometrics, meta-research, scholarship, artificial intelligence, machine learning, AI fairness, AI ethics, research on research, science studies, research software, deep learning
Any questions about this collection? Please email research@f1000.com
This Collection is associated with the Research on Research, Policy & Culture and Artificial Intelligence and Machine Learning Gateways.
Collection Advisors
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Giovanni Colavizza
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Vincent Traag
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