AI is not Social Media: But somtimes it is

AI is not social media. I keep sensing that much of the angst comes from the comparison, and I worry it may lead us to the wrong lessons and wrong actions. The anger at the arrogance of the tech bros is obvious and justified, and I share some of it. But the technology underneath works differently in ways that educators and Ed policymakers need to understand.

Social media has a main use and a main abuse. It captures attention and does not let go. Facebook decides what shows up in my feed. When I open ChatGPT, Claude, or Gemini, I bring the question, and I pick the task. That structure leaves agency with the user, and agency is at the center of what we try to build in students. We can take charge (though it has its agency traps) of AI, and we can teach our students to do it. We spent two decades watching attention platforms pull kids away from reading, sleep, and each other. A tool that helps a teacher differentiate a text, or helps a student get unstuck on a draft, runs on a different logic. A tool that helps students make, inquire, and design has potential.

Made with ChatGPT

The cost of running these models has also kept companies from chasing engagement the way social platforms did, at least so far. But the danger is still lurking in developing relationships with bots. Much like social media replacing human connections with synthetic ones at critical moments in development can have very harmful effects on kids. But unlike social media, AI offers a complete set of affordances unrelated to synthetic relationships. Moreover, you can realize the benefits without a unique login.

I am not saying the tech bros turned good. I am saying the technology is different, and our response in education should reflect that. Schools that treat AI as another attention trap will ban it and give up the chance to teach students to use it well. At the same time, the real lessons from social media, about business models and about protecting kids, still apply, and we should carry them forward. Nuance takes more effort. In classrooms, we cannot afford to skip it.

My proposition for education is to avoid a blanket ban on screens (though no phones in school sounds reasonable to me). Teach students about their own cognition and about AI. Then use AI to augment and enrich their education while emphasizing agency.

What I Actually Want From AI in Peer Review

 It’s summer, and I finally have a bit more breathing room, some of which I use for peer review (the rest is for my granddaughter). The pressure never really lets up, though. I’ve written before about how AI is reshaping that pressure, on both the writing and reviewing sides of the equation.

Publishers are drawing a clear line. They’re asking reviewers not to run submitted manuscripts through AI tools, and they’re right to ask. A paper under review is unpublished work. Feeding it into a commercial AI system risks the author’s intellectual property in ways reviewers might not stop to consider.

I follow that line carefully. I use AI to help me write up my reviews, sharpen the language, organize my thoughts, and make the feedback easier for authors to act on. I never run a manuscript I’m reviewing through AI, though often I think it could help.

I know it could be helpful because with my own writing I do something different. When I finish a draft, I ask AI to act as a peer reviewer and pick apart my argument. Where is it weak? What can I cut when I’m over a word limit? AI has been remarkably good at both. It catches gaps I’ve stopped seeing because I’ve read my own draft too many times. I get back to work the same way I would after an actual peer review, provided I agree with the notes, which, much like human reviewers, I do not have to agree with.

I do this through institutional AI that doesn’t train on what I feed it. I’ll admit, honestly, that the training question isn’t the part that keeps me up at night, even if I did not have such access; the benefits definitely outweigh the risks. What strikes me instead is how good the feedback is and how much better the review process could be if this kind of access extended to reviewers too.

I’m not calling for AI-written reviews- in that case humans are not needed. I want to be clear that we (still?) need human reviewers. What I’d love to see is publishers giving reviewers access to no-training AI as a tool.

Peer Review made with ChatGPT 5.5

Picture a reviewer working through a manuscript that cites a document by name. Right now, chasing that down means opening another tab, another search, another interruption. AI access built into the review platform could pull it up on the spot. Or picture a paper built on a statistical test with a dozen parameters. A reviewer could ask what those parameters mean and whether they were applied correctly, instead of taking the authors’ word for it or quietly moving past a section they don’t fully trust but have no time to verify.

I recently reviewed a paper where the data looked suspect. It was disappointing, and in that case, the signs were obvious enough to catch without much effort. But it left me wondering how many more subtle cases slip through, the ones that look clean on the surface and only fall apart under closer inspection. That’s exactly the kind of pattern-checking AI does well, if reviewers had a legitimate, secure way to run it.

The bigger opportunity is replication. Right now, a reviewer can, in principle, rerun an analysis or test an alternative approach if the authors shared their data. In practice, very few do it, because it takes far more than any reviewer has the time and resources for. AI with real document and data access could shrink that gap. Reviewers could actually check the math (within approximation) instead of trusting that it holds. This may be critically important when people are using AI to generate research facsimiles, sometimes fabricating the data and the writing.

None of this changes what a peer reviewer is for. The job is still to weigh the argument, judge whether the evidence supports the claims, and tell the editor whether the work deserves to move forward. AI wouldn’t replace that judgment. It would just let reviewers exercise it with more precision, on more of the paper, in less time.

Faster reviews. Sharper reviews. Reviewers who can actually verify instead of just trusting. That seems like a use of AI worth building, and one publishers are well positioned to build safely, since they control the data and access.