The field of research is undergoing significant transformations with the integration of artificial intelligence (AI) and machine learning (ML) in various aspects of scholarly communication. A notable direction is the development of frameworks and tools that facilitate the analysis and synthesis of large volumes of research data, enabling more efficient and effective knowledge discovery. Furthermore, there is a growing emphasis on addressing the social and institutional barriers that hinder the equitable distribution of AI benefits in scientific research. Researchers are also exploring innovative approaches to authorship, citation, and research evaluation, recognizing the need for more nuanced and context-dependent metrics that capture the complexities of scholarly contributions. Noteworthy papers in this regard include 'The Architecture of AI Transformation: Four Strategic Patterns and an Emerging Frontier', which proposes a framework for AI strategy and highlights the importance of collaborative intelligence, and 'AI for Scientific Discovery is a Social Problem', which argues that the primary barriers to AI adoption in science are social and institutional rather than technical.
Advancements in AI-Driven Research and Scholarly Communication
Sources
ABCD-LINK: Annotation Bootstrapping for Cross-Document Fine-Grained Links
Can we cite Wikipedia? What if Wikipedia was more reliable than its detractors ?
Charting the Future of Scholarly Knowledge with AI: A Community Perspective
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Computational Social Science and Critical Studies of Education and Technology: An Improbable Combination?
The Architecture of AI Transformation: Four Strategic Patterns and an Emerging Frontier
Authorship-contribution normalized Sh-index and citations are better research output indicators
The changing role of cited papers over time: An analysis of highly cited papers based on a large full-text dataset
Toward Robust URL Extraction for Open Science: A Study of arXiv File Formats and Temporal Trends
Finding your MUSE: Mining Unexpected Solutions Engine
Authorship Without Writing: Large Language Models and the Senior Author Analogy
Compare: A Framework for Scientific Comparisons
AI for Scientific Discovery is a Social Problem
Algorithmic Tradeoffs, Applied NLP, and the State-of-the-Art Fallacy
Causal evidence of racial and institutional biases in accessing paywalled articles and scientific data
Towards Knowledge-Aware Document Systems: Modeling Semantic Coverage Relations via Answerability Detection
Who Gets Seen in the Age of AI? Adoption Patterns of Large Language Models in Scholarly Writing and Citation Outcomes