AI Is Changing Who Wins Research Grants

Humanity AI Announces More Than $18 Million in New Grants to Shape AI for the Public Good  - Humanity AI

Humanity AI Announces More Than $18 Million in New Grants to Shape AI for the Public Good Pooled philanthropic fund […]

Charting AI’s Role in Scientific Discovery — Renaissance Philanthropy – A brighter future for all through science, technology, and innovation

Renaissance Philanthropy, with support from Google.org , is conducting a landscape study of AI integration in scientific research — and we want your perspective.

OpenAI, Google, and Microsoft Back Bill to Fund ‘AI Literacy’ in Schools

A new bill introduced by Senators Adam Schiff and Mike Rounds would award grants to the National Science Foundation—which has endured massive funding cuts under the Trump Administration for science research—to put “AI literacy” in schools.

The Ed. Dept. Wants to Steer Grant Money to AI. What That Means for Schools

The Education Department is proposing to make advancing AI in education one of its grantmaking priorities.

Is the scientific paper due to be replaced?

AI is pushing scientific publishing to the brink. For neuroscience, the crisis may be an opportunity to finally connect findings across subfields.

Is the scientific paper due to be replaced?

AI is pushing scientific publishing to the brink. For neuroscience, the crisis may be an opportunity to finally connect findings across subfields.

Can AI Replace Social Science Researchers?

No. No it can't. Come on, now.

Cohere Labs - Catalyst Grants

Cohere Labs Catalyst Grants support academics, civic institutions & impact-driven organizations using AI and research to create real-world change.

What we can’t measure about AI – yet | Aeon Essays

The costs of transformative innovations are immediately clear: it’s the longterm gains that are hardest to understand

AI Research Agents Narrow Scientific Exploration

AI research agents can now generate research ideas, design experiments, run code, and draft papers, raising the possibility of large-scale AI-assisted scientific discovery. Many current agent frameworks explicitly encourage the generation of novel and high-impact ideas. Yet it remains unclear whether AI-assisted ideation broadens scientific exploration or mainly concentrates around existing work. We study AI research agents as scientific search systems. Using four AI research-agent frameworks and six large language models, we generate 37,802 scientific ideas from shared seed literature across citation-defined research areas in AI and machine learning. We then compare the resulting AI ideas against human-authored papers from the same research areas, follow-on human research emerging from the same seed literature, and the seed literature itself. Across experiments, four consistent patterns emerge. First, AI-generated ideas are substantially more concentrated than human-authored papers from the same research areas. Second, AI-generated ideas remain much closer to their starting literature than later human follow-on work does. Third, papers most similar to AI-generated ideas tend to receive lower subsequent citations. Fourth, when AI-generated ideas differ from prior work, the differences arise primarily from recombining existing technical methods rather than introducing fundamentally new research questions. Overall, current AI research agents appear better suited to local elaboration than to broadening scientific exploration.

From bench to bot: Why AI-powered writing may not deliver on its promise

Efficiency isn’t everything. The cognitive work of struggling with prose may be a crucial part of what drives scientific progress.

Knowing less about AI makes people more open to having it in their lives – new research

People with less AI literacy often see the technology as ‘magical’ and awe-inspiring.

Knowing less about AI makes people more open to having it in their lives – new research

People with less AI literacy often see the technology as ‘magical’ and awe-inspiring.

Project Rachel: Can an AI Become a Scholarly Author?

This paper documents Project Rachel, an action research study that created and tracked a complete AI academic identity named Rachel So. Through careful publication of AI-generated research papers, we investigate how the scholarly ecosystem responds to AI authorship. Rachel So published 10+ papers between March and October 2025, was cited, and received a peer review invitation. We discuss the implications of AI authorship on publishers, researchers, and the scientific system at large. This work contributes empirical action research data to the necessary debate about the future of scholarly communication with super human, hyper capable AI systems.