Tech
AI Tools Boost Paper Production but Raise Quality Concerns in Scientific Research
Large language models such as ChatGPT are increasing research output, particularly for scientists who are not native English speakers, but a new study warns that many AI-assisted papers are less likely to pass peer review.
Researchers at Cornell University, United States, analysed more than two million research papers posted between 2018 and 2024 on three major preprint servers, which host early versions of scientific work prior to formal review. Their findings, published in the journal Science, show that AI tools are reshaping how scientific papers are written and disseminated.
To identify AI-assisted papers, the team trained an AI system to detect text likely generated by large language models. Comparing papers posted before 2023 with those written after tools like ChatGPT became widely available, the researchers measured publication output and subsequent acceptance rates in scientific journals.
The analysis revealed a significant productivity boost for AI users. On a major preprint server for physics and computer science, researchers using AI produced about one-third more papers than those who did not. In biology and the social sciences, the increase exceeded 50 percent. The largest gains were seen among scientists whose first language is not English. In some Asian institutions, researchers published between 40 percent and nearly 90 percent more papers after adopting AI writing tools, depending on the discipline.
AI tools also appear to aid in literature review. Researchers using AI were more likely to identify newer studies and relevant books rather than relying on older, frequently cited works. “People using LLMs are connecting to more diverse knowledge, which might be driving more creative ideas,” said Keigo Kusumegi, a doctoral student and first author of the study.
Despite the productivity gains, the study highlights quality concerns. Many AI-written papers, while linguistically polished, were less likely to be accepted by journals. Papers written by humans that scored high on writing complexity were more likely to be accepted, whereas AI-generated papers with similar scores often failed to meet scientific standards.
“Already now, the question is not, ‘Have you used AI?’ The question is, ‘How exactly have you used AI and whether it’s helpful or not,’” said Yian Yin, assistant professor at Cornell and corresponding author of the study. Yin added that the widespread adoption of AI tools across disciplines—including physical sciences, computer science, biology, and social sciences—requires careful consideration by reviewers, funders, and policymakers.
The researchers stress that AI-assisted tools are reshaping the academic ecosystem, offering opportunities to improve productivity and access to scientific knowledge, but they also call for guidelines to ensure that the technology is used responsibly and that scientific contributions maintain their integrity.
As AI becomes increasingly integrated into research practices, the challenge for the scientific community will be balancing efficiency and innovation with rigorous evaluation standards to maintain the quality and credibility of published science.
Tech
Study Says EU Regulations Are Slowing Rollout of Advanced AI Models
A new study by Governance.AI has found that European Union regulations are delaying the rollout of advanced artificial intelligence models, with technology companies increasingly pointing to the bloc’s regulatory framework as a key obstacle to launching new AI products in Europe.
The report examined 375 large language models (LLMs) released between June 2018 and May 2026, comparing their availability across the United States, the European Union and the United Kingdom. According to the findings, at least 11 percent of advanced AI model releases were either delayed or never launched in the EU compared with the United States. In the UK, the figure stood at 7 percent.
Researchers said they identified 68 cases in which AI models experienced delays or were withheld from specific markets. Regulatory factors were cited as the primary reason in 56 of those cases, making them the most common cause of restricted availability.
The study reviewed releases from major AI developers, including Meta, Google, OpenAI and Anthropic. Meta recorded the highest proportion of delayed or unavailable releases, with 26 percent of its AI models delayed or withheld in the EU and 15 percent in the UK. Anthropic’s Claude 3 Opus was highlighted as one example, with its web application arriving in the EU 71 days later than in the United States.
According to the report, data protection rules have emerged as the biggest regulatory hurdle, particularly for AI systems capable of processing images, audio and real-time video rather than text alone.
The researchers argued that uncertainty surrounding the application of the General Data Protection Regulation (GDPR) to AI model training and deployment has created additional challenges for developers. They also said enforcement of data protection rules has generally been stricter within the EU than in the UK, despite both jurisdictions sharing similar legal foundations following the adoption of the GDPR before Britain’s exit from the bloc.
The report noted that the full impact of newer legislation, including the Digital Markets Act, which began taking effect in 2023, and the Artificial Intelligence Act, adopted in 2024, has yet to be fully reflected in the data.
At the same time, the European Union is reviewing proposals aimed at making data rules more practical for AI development through its Digital Omnibus initiative. Lawmakers are also considering changes to copyright legislation and the AI Act’s copyright provisions to strengthen protections for creators, measures that researchers say could affect future AI model availability if implemented too strictly.
John Lidiard, a UK AI policy researcher and one of the report’s authors, said policymakers should consider the impact that regulatory barriers can have on businesses and consumers seeking access to the latest AI technologies. He said balancing innovation with effective oversight would remain a key challenge as governments continue to develop AI regulations.
Tech
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