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The role of AI in peer review should be acknowledged in order to ensure practical guidance and policies that help scholars respond with consistency and confidence.
AIs on their own found ways to exploit regulations and evade current safeguards.
Queen Mary University researchers have developed a new AI-powered framework designed to help scientists review and analyse vast amounts of literature faster, more transparently, and with greater human oversight.
International Mathematical Union endorses warning about tech industry influence.
In penning their national AI strategies governments are not only deciding how to regulate AI. They are also defining what AI should deliver, from economic growth to public-sector transformation.
This UCS blog post challenges the narrative that AI will be a climate savior, cautioning that focusing on hypothetical future tech could distract from proven climate solutions.
As proponents of AI claim it will soon replace software engineers, what does this mean for qualitative researchers and research software development?
Can Large Language Models (LLMs) support the process or validate expert evaluations? ChatGPT-4o mini scores correlated positively with expert scores in almost all 34 (field-based) Units of Assessment (UoAs).
Largest illegal database of scientific papers has gaps in recently published literature, but its chatbot can still prove useful—especially for less-timely questions
As AI systems increasingly reason from the scientific literature, the integrity signals that make research trustworthy - open data, structured metadata, robust retraction processes - matter more than ever.
Jim Dickinson presents findings from new research on students' use of AI – and argues the sector is punishing precisely the disposition it should be cultivating.
If we were designing peer review from scratch for a world where powerful LLMs exist, what would we actually need humans for, and what could we comfortably automate?
