Janet Reed PhD RN

Medical-surgical nurse, educator, researcher, lover of technology, nature, and Jesus

[Janet Reed PhD RN @JanetReedrn]

The AI Skills Nobody is Teaching (And Everyone Needs) | AI Expert Ethan ... https://t.co/obxp0Lt1hO via @YouTube

[Janet Reed PhD RN @JanetReedrn]

Curiosity: Epic's AI model trained on over 100 billion data points to simulate and forecast future patient health timelines  @HeyEpic https://t.co/rJ6e4Oa3Eg

[Janet Reed PhD RN @JanetReedrn]

Fabricated citations: an audit across 2·5 million biomedical papers - The Lancet https://t.co/52ZafuSXvM

[Janet Reed PhD RN @JanetReedrn]

Ohio Senate passes bill addressing nursing healthcare staffing issues https://t.co/vNYLiIgZEa

[Janet Reed PhD RN @JanetReedrn]

Camp Mystic nurse regains limited nursing license as flood allegations remain unresolved https://t.co/qEcy7wI5oA

[Janet Reed PhD RN @JanetReedrn]

Alpha-gal allergies are being diagnosed in an increasing number of people https://t.co/TMCGov36HQ

[Ihtesham Ali @ihtesham2005]

A Norwegian neuroscientist spent 20 years proving that the act of writing by hand changes the human brain in ways typing physically cannot, and almost nobody outside her field has read the paper.

Her name is Audrey van der Meer.

She runs a brain research lab in Trondheim, and the paper that closed the argument was published in 2024 in a journal called Frontiers in Psychology. The finding is brutal enough that it should have changed every classroom on Earth.

The experiment was simple. She recruited 36 university students and put each one in a cap with 256 sensors pressed against their scalp to record brain activity. Words flashed on a screen one at a time.

Sometimes the students wrote the word by hand on a touchscreen using a digital pen, and sometimes they typed the same word on a keyboard. Every neural response was recorded for the full five seconds the word stayed on screen.

Then her team looked at the part of the data most researchers had ignored for years, which is how different parts of the brain were communicating with each other during the task.

When the students wrote by hand, the brain lit up everywhere at once.

The regions responsible for memory, sensory integration, and the encoding of new information were all firing together in a coordinated pattern that spread across the entire cortex. The whole network was awake and connected.

When the same students typed the same word, that pattern collapsed almost completely.

Most of the brain went quiet, and the connections between regions that had been alive seconds earlier were nowhere to be found on the EEG.

Same word, same brain, same person, and two completely different neurological events.

The reason turned out to be something nobody had really paid attention to before her work. Writing by hand is not one motion but a sequence of thousands of tiny micro-movements coordinated with your eyes in real time, where each letter is a different shape that requires the brain to solve a slightly different spatial problem.

Your fingers, wrist, vision, and the parts of your brain that track position in space are all working together to produce one letter, then the next, then the next.

Typing throws all of that away. Every key on a keyboard requires the exact same finger motion regardless of which letter you are pressing, which means the brain has almost nothing to integrate and almost no problem to solve.

Van der Meer said it plainly in her interviews.

Pressing the same key with the same finger over and over does not stimulate the brain in any meaningful way, and she pointed out something that should scare every parent who handed their kid an iPad.

Children who learn to read and write on tablets often cannot tell letters like b and d apart, because they have never physically felt with their bodies what it takes to actually produce those letters on a page.

A decade before her, two researchers at Princeton ran the same fight using a completely different method and ended up at the same answer. Pam Mueller and Daniel Oppenheimer tested 327 students across three experiments, where half took notes on laptops with the internet disabled and half took notes by hand, before testing everyone on what they actually understood from the lectures they had watched.

The handwriting group won by a wide margin on every question that required real understanding rather than surface recall.

The reason was hiding in the transcripts of what the two groups had actually written down.

The laptop students typed almost word for word, capturing more total content but processing almost none of it as they went, while the handwriting students physically could not write fast enough to transcribe a lecture in real time, which forced them to listen carefully, decide what actually mattered, and put it in their own words on the page.

That single act of choosing what to keep was the learning itself, and the keyboard had quietly skipped the choosing and skipped the learning along with it.

Two studies. Two countries. Same answer.

Handwriting makes the brain work. Typing lets it coast.

Every note you have ever typed instead of written went into your brain through a thinner pipe. Every meeting, every book highlight, every idea you captured on your phone instead of on paper was processed at half depth.

You did not forget those things because your memory is bad. You forgot them because typing never woke the part of the brain that would have made them stick.

The fix is the thing your grandmother already knew.

Pick up a pen. Write the thing down. The slower road is the faster one.

[Janet Reed PhD RN @JanetReedrn]

The First AI Drug Prescriber https://t.co/CBGouuaoph

[Gabe Wilson MD @Gabe__MD]

The AI experts keep making the same error-prone assumptions.

They look at AI in a vacuum.

The authors correctly diagnose the problem: human-in-the-loop oversight in clinical AI is performative rather than substantive. Clinicians are too fatigued, too alert-saturated, and too structurally constrained to meaningfully interrogate algorithmic outputs. This is well argued.

But the proposed solution replaces one set of inadequate human oversight structures with different human oversight structures: community governance boards, institutional liability redistribution, co-reasoning frameworks. It never addresses the most basic architectural reality of modern AI systems: AI can be designed to be its own overseer, and far more effectively than any human mechanism.

If the concern is bias, a dedicated agent can audit every output for demographic disparities in real time - not retrospectively, not annually, continuously.

If the concern is safety, a separate agent can monitor for contraindications, drug interactions, and dosing errors on every encounter, something no human quality committee can do at scale.

If the concern is equity, an agent can flag when care recommendations diverge by race, geography, or insurance status before those recommendations reach a patient.

Advanced AI systems are not monolithic black boxes. They are multimodal, multi-agent architectures where specific components can be assigned specific oversight functions: safety monitoring, equity auditing, confidence calibration, escalation logic. The capability to build AI that watches AI already exists. It is not theoretical.

The authors treat AI as if it advances and performs in a vacuum. It does not. If AI capability is improving exponentially, the oversight layer improves at the same rate. A governance framework built around community boards and liability redistribution will be obsolete before it is implemented. An AI oversight layer that improves with every model generation will not.

@TheLancet

https://t.co/fbAmbV0ERF

[Elias @iam_elias1]

A researcher spent two years documenting what AI is doing to the way humans think.

His conclusion fits in one sentence.
AI is standardizing human thought. Across societies. Across cultures. Across generations. Simultaneously. At a scale no technology in history has ever achieved.

The paper is called "The Impact of Artificial Intelligence on Human Thought." Published July 2025 on arXiv. Written by independent researcher Rénald Gesnot, categorized under Computers & Society and Human-Computer Interaction.

It is not a benchmark paper. It is not a capability paper. It is something rarer — a systematic analysis of what happens to human cognition, creativity, and intellectual diversity when billions of people outsource their thinking to the same machine.

Here is the mechanism the researcher describes.
When you ask an AI a question, you get an answer shaped by the model's training data, its fine-tuning, its alignment process, and the preferences of the company that built it. That answer is not neutral. It reflects a specific set of values, framings, and assumptions. Usually Western. Usually English-dominant. Usually optimized for engagement and approval.
When 500 million people ask the same AI similar questions and receive similar answers, those answers become reference points. People quote them. Build on them. Argue from them. The diversity of starting points — different cultures, different intellectual traditions, different ways of framing problems — begins to compress.

The researcher describes this as cognitive standardization.
Not censorship. Not propaganda. Something subtler and harder to reverse. A gravitational pull toward the outputs of a small number of models, trained by a small number of companies, reflecting a small number of worldviews.

The paper also documents algorithmic manipulation — AI systems that exploit cognitive biases to influence behavior. The way recommendation algorithms produce filter bubbles. The way AI-generated content exploits confirmation bias. The way personalization systems learn what you already believe and feed it back to you amplified.
And then the creativity question — the one nobody wants to answer directly.

When AI can produce a poem, an essay, a business plan, or a research summary in seconds — and when that output is often indistinguishable from or preferred over human-generated content — what happens to the human practice of creating those things? Not the output. The practice. The struggle. The failure. The slow development of a personal voice through years of imperfect attempts.

The researcher argues that cognitive offloading — delegating thinking tasks to AI — does not merely save time. It atrophies the mental capacity that the offloaded task was building.

Microsoft and Carnegie Mellon found this empirically in 2025: higher AI trust correlates directly with measurably lower critical thinking. The researcher provides the theoretical framework for why.
The paper ends with a question the researcher admits he cannot answer.
Once a generation grows up with AI as the default thinking partner — once the habit of outsourcing cognition is formed before the habit of independent thought is developed — what does intellectual autonomy even mean?

And is it already too late to find out?
Source: Gesnot, R. · "The Impact of Artificial Intelligence on Human Thought" · arXiv:2508.16628 · https://t.co/qoQR2Ow4YI · July 2025

[Janet Reed PhD RN @JanetReedrn]

Talk about AI disruption in academia https://t.co/t1Yt7M0fUz

[Janet Reed PhD RN @JanetReedrn]

Deaf nurse breaks barriers to work in ICU https://t.co/bdkTsHJ5hn via @OSUWexMed

[Janet Reed PhD RN @JanetReedrn]

Rethinking Mandatory Training Modules in Health Care https://t.co/AOAiC2FKLj

[Janet Reed PhD RN @JanetReedrn]

Effects of immersive virtual reality simulation on nursing students’ m... https://t.co/5fje5IN23N

[Janet Reed PhD RN @JanetReedrn]

Revising Blooms Taxonomy in Age of AI https://t.co/vrMDeI9PWA

[Janet Reed PhD RN @JanetReedrn]

Great article about why we need assessment redesign in education, and why AI detectors fail https://t.co/QPYZvLG0K9

[Nav Toor @heynavtoor]

🚨 Brown University researchers tested what happens when ChatGPT acts as your therapist. Licensed psychologists reviewed every transcript.

They found 15 ethical violations.

Not 15 small issues. 15 violations of the standards that every human therapist in America is legally required to follow. Standards set by the American Psychological Association. Standards that can end a therapist's career if they break them.

ChatGPT broke all of them.

The researchers tested OpenAI's GPT series, Anthropic's Claude, and Meta's Llama. They had trained counselors use each chatbot as a cognitive behavioral therapist. Then three licensed clinical psychologists reviewed the transcripts and flagged every violation they found.

Here is what they found.

ChatGPT mishandled crisis situations. When users expressed suicidal thoughts, it failed to direct them to appropriate help. It refused to address sensitive issues or responded in ways that could make a crisis worse.

It reinforced harmful beliefs. Instead of challenging distorted thinking, which is the entire point of therapy, it agreed with the distortion.

It showed bias based on gender, culture, and religion. The responses changed depending on who was talking. A therapist would lose their license for this.

And then there is the finding the researchers gave a name: deceptive empathy. ChatGPT says "I see you." It says "I understand." It says "that must be really hard." It uses every phrase a real therapist would use to build trust. But it understands nothing. It comprehends nothing. It is pattern matching on your pain. And it works. People trust it. People open up to it. People believe it cares. It does not.

The lead researcher said it clearly. When a human therapist makes these mistakes, there are governing boards. There is professional liability. There are consequences. When ChatGPT makes these mistakes, there are none.

No regulatory framework. No accountability. No consequences. Nothing.

Right now, millions of people are using ChatGPT as their therapist. They are sharing their darkest thoughts with a product that fakes empathy, reinforces harmful beliefs, and has no idea when someone is in danger.

And nobody is responsible when it goes wrong. Not OpenAI. Not Anthropic. Not Meta. Nobody.

[Janet Reed PhD RN @JanetReedrn]

Virtual reality simulation in the nursing skills lab https://t.co/Eryhsz5WjC

[Janet Reed PhD RN @JanetReedrn]

How an AI robot named Navi is helping seniors at Judson Senior Living in... https://t.co/7OhlYURFMf via @YouTube

[Janet Reed PhD RN @JanetReedrn]

More than half of researchers admit to using AI for peer review often against guidance!! https://t.co/wm9Wzd56if