Learning to write is learning to think, because writing is thinking. They are two peas in a powerful pod. This article goes over my writing process, focused on minimizing the activation energy to get started.
Writing Is Thinking
When you write about your work, it makes all of us smarter for the effort, including you. Done well, this kind of sharing means you’re contributing signal, instead of noise. But writers are made, n…
Brian D. Earp, Ph.D. on Twitter / X
"Writing is thinking." This phrase went viral recently (from https://t.co/EQqohfhUMd), often quoted in the context of objections to use of AI in drafting academic prose. In Nature Reviews Bioengineering we respond: "Thinking is not only writing." Preview below. Shareable full… pic.twitter.com/cZtGdoSwqV— Brian D. Earp, Ph.D. (@briandavidearp) May 15, 2026
Writing by Hand is Good for your Brain - Here's how to do it
Because I am known to write using a fountain pen on paper, a number of people have pointed me to this post and its underlying research. I won’t rehash what is said in those sources, but the gist of it is that when you write things down by hand you’re recruiting more of your brain, which is a good thing.
Thinking is not only writing
Generative artificial intelligence (AI) raises valid concerns about the loss of cognitive work embedded in scholarly writing. However, universally dismissing AI-assisted drafting mistakenly conflates writing with thinking itself and reinforces existing inequities in who can transform their ideas into polished text. Academic authorship should depend more on the depth and creativity of human intellectual engagement than on the medium through which first drafts are produced.
From bench to bot: Why AI-powered writing may not deliver on its promise
Efficiency isn’t everything. The cognitive work of struggling with prose may be a crucial part of what drives scientific progress.
if you've never written an essay for fun, you're making yourself dumber
What’s Lost as Handwriting Fades (Published 2014)
Even as the emphasis shifts to the keyboard, experts say that learning to write by hand improves motor skills, memory and creativity.
Wherein I Find Myself Writing About Writing
Most everyone finds writing to be challenging (especially those who say they enjoy it). This is because writing is an intentional, thoughtful act. This is as it should be, but “AI” has recently exacerbated the misbelief that writing is simply an output. In fact, it's a creative process that has merit in and of itself.
Writing With and Beyond AI: Fieldnotes from Computer-Mediated Poetry
Apr 10, 2026, 12:15 pm - Large language models can make writing mind-numbingly efficient — but the point of writing with AI should be to write what we couldn’t have written alone (without generating bland, derivative “slop”).
Write Things Down
Writing things down is powerful, for humans and for AI; what comes first, however, is what to write, why to do it, and actually getting things done.
Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task – MIT Media Lab
This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and …
I knew my writing students were using AI. Their confessions led to a powerful teaching moment | Micah Nathan
The problem wasn’t just the perfectly polished, yet mediocre prose. It’s what’s lost when we surrender the struggle to translate thought into words
How to write well with AI
Why people who pledge never to write with AI are telling on themselves
Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools). Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM). A total of 54 participants took part in Sessions 1-3, with 18 completing session 4. We used electroencephalography (EEG) to assess cognitive load during essay writing, and analyzed essays using NLP, as well as scoring essays with the help from human teachers and an AI judge. Across groups, NERs, n-gram patterns, and topic ontology showed within-group homogeneity. EEG revealed significant differences in brain connectivity: Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity. Cognitive activity scaled down in relation to external tool use. In session 4, LLM-to-Brain participants showed reduced alpha and beta connectivity, indicating under-engagement. Brain-to-LLM users exhibited higher memory recall and activation of occipito-parietal and prefrontal areas, similar to Search Engine users. Self-reported ownership of essays was the lowest in the LLM group and the highest in the Brain-only group. LLM users also struggled to accurately quote their own work. While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.
Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools). Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM). A total of 54 participants took part in Sessions 1-3, with 18 completing session 4. We used electroencephalography (EEG) to assess cognitive load during essay writing, and analyzed essays using NLP, as well as scoring essays with the help from human teachers and an AI judge. Across groups, NERs, n-gram patterns, and topic ontology showed within-group homogeneity. EEG revealed significant differences in brain connectivity: Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity. Cognitive activity scaled down in relation to external tool use. In session 4, LLM-to-Brain participants showed reduced alpha and beta connectivity, indicating under-engagement. Brain-to-LLM users exhibited higher memory recall and activation of occipito-parietal and prefrontal areas, similar to Search Engine users. Self-reported ownership of essays was the lowest in the LLM group and the highest in the Brain-only group. LLM users also struggled to accurately quote their own work. While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.