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Generative AI tools are increasingly entering academic peer review workflows, raising questions about fairness, accountability, and the legitimacy of evaluative judgment. While these systems promise efficiency gains amid growing reviewer…
This study empirically investigates the impact of AI-augmented peer review systems on scientific productivity using panel data from OECD countries. While prior research has highlighted inefficiencies in traditional peer review, little…
The integration of large language models (LLMs) into peer review raises a concern beyond authorship and detection: the potential cascading automation of the entire editorial process. As reviews become partially or fully machine-generated,…
Peer review in software engineering research operates under tight time constraints, while generative AI has substantially reduced the human effort required to produce polished research narratives. Reviewer attention is often spent on…
Research in AI evaluation has grown increasingly complex and multidisciplinary, attracting researchers with diverse backgrounds and objectives. As a result, divergent evaluation paradigms have emerged, often developing in isolation,…
The scholarly publishing ecosystem faces a dual crisis of unmanageable submission volumes and unregulated AI, creating an urgent need for new governance models to safeguard scientific integrity. The traditional human-only peer review regime…
Generative Artificial Intelligence (AI) holds immense potential in medical applications. Numerous studies have explored the efficacy of various generative AI models within healthcare contexts, but there is a lack of a comprehensive and…
Quantitative Artificial Intelligence (AI) Benchmarks have emerged as fundamental tools for evaluating the performance, capability, and safety of AI models and systems. Currently, they shape the direction of AI development and are playing an…
A number of scientific conferences and journals have recently enacted policies that prohibit LLM usage by peer reviewers, except for polishing, paraphrasing, and grammar correction of otherwise human-written reviews. But, are these policies…
Scholarly publishing faces increasingly strong stressors, including submission overload, reviewer fatigue, inconsistent evaluation, governance opacity, and vulnerability to manipulation in old and new forms. While recent studies applied…
The arrival of deep learning techniques able to infer patterns from large datasets has dramatically improved the performance of Artificial Intelligence (AI) systems. Deep learning's rapid development and adoption, in great part led by large…
The implementation of responsible AI in an organization is inherently complex due to the involvement of multiple stakeholders, each with their unique set of goals and responsibilities across the entire AI lifecycle. These responsibilities…
Artificial intelligence (AI) has been used to advance different fields, such as education, healthcare, and finance. However, the application of AI in the field of project management (PM) has not progressed equally. This paper reports on a…
Peer review is fundamental to scientific research, but the growing volume of publications has intensified the challenges of this expertise-intensive process. While LLMs show promise in various scientific tasks, their potential to assist…
Artificial intelligence systems are transforming scientific discovery by accelerating specific research tasks, from protein structure prediction to materials design, yet remain confined to narrow domains requiring substantial human…
Peer review serves as the gatekeeper of science, yet the surge in submissions and widespread adoption of large language models (LLMs) in scholarly evaluation present unprecedented challenges. While recent work has focused on using LLMs to…
Peer review is vital in academia for evaluating research quality. Top AI conferences use reviewer confidence scores to ensure review reliability, but existing studies lack fine-grained analysis of text-score consistency, potentially missing…
Artificial intelligence systems are increasingly deployed in biomedical research. However, current evaluation frameworks may inadequately assess their effectiveness as research collaborators. This rapid review examines benchmarking…
Many promising-looking ideas in AI research fail to deliver, but their validation takes substantial human labor and compute. Predicting an idea's chance of success is thus crucial for accelerating empirical AI research, a skill that even…
Evaluating AI-generated reviews by verdict agreement is widely recognized as insufficient, yet current alternatives rarely audit which concerns a system identifies, how it prioritizes them, or whether those priorities align with the review…