From 19th-century beef tea to paper mills and generative AI, the challenge is no longer detection alone; it is preventing bad claims from becoming embedded knowledge.
The central risk of AI in scholarly publishing is therefore not hallucination but cognitive debt: the gradual erosion of the human capacities that give scholarship its originality, integrity, and meaning.
AI is now a permanent feature of the scholarly landscape. The question is no longer whether it should be used, but how it should be understood and governed.
Today Alice Meadows interviews Jennifer Fleet (Aries Systems) and Christian Gruback (ChronosHub) about changing author needs and how their recently announced partnership is an effort to address them.
What happens then to “author voice,” which has always been an important consideration to authors and reviewers alike, if AI is helping researchers with their writing?
Today’s guest post warns the community that, in the AI era, we cannot keep building the pieces that lay the foundation and letting someone else own what gets built on top of it.
Robert Harington reflects on our addiction to speed and advocates for slow scholarly publishing and the inherent beauty of not always being first.
Open Science 2.0 must focus not only on access, but also on trust, interpretation, learning, and effective communication. The challenge facing the scholarly publishing ecosystem is ensuring that what is open is also trustworthy, understandable, and genuinely useful.
Today’s guest post presents outcomes of the WCRI’s (World Conferences on Research Integrity) Focus Track to address this gap in clarity and standardization of AI disclosure.
Today’s post asks us to acknowledge the role of AI in peer review and ensure practical guidance and policies that help scholars respond with consistency and confidence.
The future of scholarly communication will not be determined by how powerful AI becomes, but by whether the research community remains clear about the purpose those capabilities are meant to serve and whether it can govern them together.
A Cambridge workshop proposes new standard work to support provenance, attribution and metrics in scholarly communications AI tools.
While it’s true that AI may be viewed as “legitimate,” it’s far from universally loved. Understanding that distinction tells us something important about how the technology may ultimately be adopted — and governed — within academia.
Today’s post explores what happens to the scholarly content platform when AI agents become the users.
Today, we offer reflections on the SSP Annual Meeting from our 2026 Fellowship cohort.