The Changing Face of Peer Review

In the world of academic publishing, peer review has long been the gold standard. It’s the firewall that helps keep junk science out of journals, ensuring that only solid, well-supported work sees the light of day. But as technology evolves, so does the way we evaluate research. And lately, something’s shifting: human reviewers—the folks who used to comb through every submission with a red pen and a skeptical eye—are increasingly being replaced or supported by algorithms and artificial intelligence (AI). The Scholarly Kitchen brought this topic to our attention in their article, “Peer Review Has Lost Its Human Face. So, What’s Next?“

So, what exactly is changing? And what are we losing (or gaining) in the process?

AI tools are now being used in all kinds of ways to “enhance” peer review. Some journals use them to scan for plagiarism, verify citations or assess statistical accuracy. Others are experimenting with tools that rank novelty, evaluate writing quality or even simulate reviewer feedback.

In fact, some platforms are experimenting with fully automated review systems—where human reviewers are barely involved at all, or only step in at the very end.

While automation offers efficiency, there’s something inherently valuable about human review that machines just can’t replicate. Contextual judgment, creativity, ethical insight, are a few.

Most experts agree we’re not replacing human reviewers entirely—at least not yet. Instead, we’re likely moving toward a hybrid model, where AI handles the heavy lifting (plagiarism checks, statistical verification, etc.), and human reviewers focus on big-picture thinking and critical analysis.

Still, it’s clear that the peer review system is being reshaped. And while the shift might fix some longstanding problems (like reviewer overload and long turnaround times), it also raises new questions: Who programs the AI? What biases are baked into those systems? And what happens when a machine tells a researcher their life’s work isn’t worth publishing?

Access Innovations knows information science and scholarly publishing. We also know AI and are uniquely positioned to get the most out of the new AI engines. We use various techniques to enhance your data and train focused language models so that you get better results.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.