Can you tell when a piece of writing was generated by AI? Roger J. Kreuz takes a closer look at the surprisingly tricky task of identifying ChatGPT-authored texts. From telltale quirks like overused em dashes to oddly formal word choices (“delves,” anyone?), he argues that detection remains more interpretive art than hard science. A fascinating read for editors, educators, and anyone thinking critically about authorship in the age of generative AI.

People are now routinely using chatbots to write computer code, summarize articles and books, or solicit advice. But these chatbots are also employed to quickly generate text from scratch, with some users passing off the words as their own.

This has, not surprisingly, created headaches for teachers tasked with evaluating their students’ written work. It’s also created issues for people seeking advice on forums like Reddit, or consulting product reviews before making a purchase.

Over the past few years, researchers have been exploring whether it’s even possible to distinguish human writing from artificial intelligence-generated text. But the best strategies to distinguish between the two may come from the chatbots themselves.

Too good to be human?

Several recent studies have highlighted just how difficult it is to determine whether text was generated by a human or a chatbot.

Research participants recruited for a 2021 online study, for example, were unable to distinguish between human- and ChatGPT-generated stories, news articles and recipes.

Language experts fare no better. In a 2023 study, editorial board members for top linguistics journals were unable to determine which article abstracts had been written by humans and which were generated by ChatGPT. And a 2024 study found that 94% of undergraduate exams written by ChatGPT went undetected by graders at a British university.

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Clearly, humans aren’t very good at this.

A commonly held belief is that rare or unusual words can serve as “tells” regarding authorship, just as a poker player might somehow give away that they hold a winning hand.

Researchers have, in fact, documented a dramatic increase in relatively uncommon words, such as “delves” or “crucial,” in articles published in scientific journals over the past couple of years. This suggests that unusual terms could serve as tells that generative AI has been used. It also implies that some researchers are actively using bots to write or edit parts of their submissions to academic journals. Whether this practice reflects wrongdoing is up for debate.

In another study, researchers asked people about characteristics they associate with chatbot-generated text. Many participants pointed to the excessive use of em dashes – an elongated dash used to set off text or serve as a break in thought – as one marker of computer-generated output. But even in this study, the participants’ rate of AI detection was only marginally better than chance.

Given such poor performance, why do so many people believe that em dashes are a clear tell for chatbots? Perhaps it’s because this form of punctuation is primarily employed by experienced writers. In other words, people may believe that writing that is “too good” must be artificially generated.

But if people can’t intuitively tell the difference, perhaps there are other methods for determining human versus artificial authorship.

Stylometry to the rescue?

Some answers may be found in the field of stylometry, in which researchers employ statistical methods to detect variations in the writing styles of authors.

I’m a cognitive scientist who authored a book on the history of stylometric techniques. In it, I document how researchers developed methods to establish authorship in contested cases, or to determine who may have written anonymous texts.

One tool for determining authorship was proposed by the Australian scholar John Burrows. He developed Burrows’ Delta, a computerized technique that examines the relative frequency of common words, as opposed to rare ones, that appear in different texts.

It may seem counterintuitive to think that someone’s use of words like “the,” “and” or “to” can determine authorship, but the technique has been impressively effective.

Burrows’ Delta, for example, was used to establish that Ruth Plumly Thompson, L. Frank Baum’s successor, was the author of a disputed book in the “Wizard of Oz” series. It was also used to determine that love letters attributed to Confederate Gen. George Pickett were actually the inventions of his widow, LaSalle Corbell Pickett.

A major drawback of Burrows’ Delta and similar techniques is that they require a fairly large amount of text to reliably distinguish between authors. A 2016 study found that at least 1,000 words from each author may be required. A relatively short student essay, therefore, wouldn’t provide enough input for a statistical technique to work its attribution magic.

More recent work has made use of what are known as BERT language models, which are trained on large amounts of human- and chatbot-generated text. The models learn the patterns that are common in each type of writing, and they can be much more discriminating than people: The best ones are between 80% and 98% accurate.

However, these machine-learning models are “black boxes” – that is, we don’t really know which features of texts are responsible for their impressive abilities. Researchers are actively trying to find ways to make sense of them, but for now, it isn’t clear whether the models are detecting specific, reliable signals that humans can look for on their own.

A moving target

Another challenge for identifying bot-generated text is that the models themselves are constantly changing – sometimes in major ways.

Early in 2025, for example, users began to express concerns that ChatGPT had become overly obsequious, with mundane queries deemed “amazing” or “fantastic.” OpenAI addressed the issue by rolling back some changes it had made.

Of course, the writing style of a human author may change over time as well, but it typically does so more gradually.

At some point, I wondered what the bots had to say for themselves. I asked ChatGPT-4o: “How can I tell if some prose was generated by ChatGPT? Does it have any ‘tells,’ such as characteristic word choice or punctuation?”

The bot admitted that distinguishing human from nonhuman prose “can be tricky.” Nevertheless, it did provide me with a 10-item list, replete with examples.

These included the use of hedges – words like “often” and “generally” – as well as redundancy, an overreliance on lists and a “polished, neutral tone.” It did mention “predictable vocabulary,” which included certain adjectives such as “significant” and “notable,” along with academic terms like “implication” and “complexity.” However, though it noted that these features of chatbot-generated text are common, it concluded that “none are definitive on their own.”

Chatbots are known to hallucinate, or make factual errors.

But when it comes to talking about themselves, they appear to be surprisingly perceptive.

As the U.S. signals a retreat from global climate leadership, other nations are stepping into the vacuum. In this article from The Conversation, political science professor Sarah J. Hummel explores how countries like China are taking the lead in international climate negotiations and investment, reshaping the power dynamics of global climate governance. For those working in or adjacent to international policy, sustainability, or global development, it’s a timely reminder that leadership can shift — and often does — when others step back.

When President Donald Trump announced in early 2025 that he was withdrawing the U.S. from the Paris climate agreement for the second time, it triggered fears that the move would undermine global efforts to slow climate change and diminish America’s global influence.

A big question hung in the air: Who would step into the leadership vacuum?

I study the dynamics of global environmental politics, including through the United Nations climate negotiations. While it’s still too early to fully assess the long-term impact of the United States’ political shift when it comes to global cooperation on climate change, there are signs that a new set of leaders is rising to the occasion.

World responds to another US withdrawal

The U.S. first committed to the Paris Agreement in a joint announcement by President Barack Obama and China’s Xi Jinping in 2015. At the time, the U.S. agreed to reduce its greenhouse gas emissions 26% to 28% below 2005 levels by 2025 and pledged financial support to help developing countries adapt to climate risks and embrace renewable energy.

Some people praised the U.S. engagement, while others criticized the original commitment as too weak. Since then, the U.S. has cut emissions by 17.2% below 2005 levels – missing the goal, in part because its efforts have been stymied along the way.

Just two years after the landmark Paris Agreement, Trump stood in the Rose Garden in 2017 and announced he was withdrawing the U.S. from the treaty, citing concerns that jobs would be lost, that meeting the goals would be an economic burden, and that it wouldn’t be fair because China, the world’s largest emitter today, wasn’t projected to start reducing its emissions for several years.

Scientists and some politicians and business leaders were quick to criticize the decision, calling it “shortsighted” and “reckless.” Some feared that the Paris Agreement, signed by almost every country, would fall apart.

But it did not.

In the United States, businesses such as Apple, Google, Microsoft and Tesla made their own pledges to meet the Paris Agreement goals.

Hawaii passed legislation to become the first state to align with the agreement. A coalition of U.S. cities and states banded together to form the United States Climate Alliance to keep working to slow climate change.

Globally, leaders from Italy, Germany and France rebutted Trump’s assertion that the Paris Agreement could be renegotiated. Others from Japan, Canada, Australia and New Zealand doubled down on their own support of the global climate accord. In 2020, President Joe Biden brought the U.S. back into the agreement.

Now, with Trump pulling the U.S. out again – and taking steps to eliminate U.S. climate policies, boost fossil fuels and slow the growth of clean energy at home – other countries are stepping up.

On July 24, 2025, China and the European Union issued a joint statement vowing to strengthen their climate targets and meet them. They alluded to the U.S., referring to “the fluid and turbulent international situation today” in saying that “the major economies … must step up efforts to address climate change.”

In some respects, this is a strength of the Paris Agreement – it is a legally nonbinding agreement based on what each country decides to commit to. Its flexibility keeps it alive, as the withdrawal of a single member does not trigger immediate sanctions, nor does it render the actions of others obsolete.

The agreement survived the first U.S. withdrawal, and so far, all signs point to it surviving the second one.

Who’s filling the leadership vacuum

From what I’ve seen in international climate meetings and my team’s research, it appears that most countries are moving forward.

One bloc emerging as a powerful voice in negotiations is the Like-Minded Group of Developing Countries – a group of low- and middle-income countries that includes China, India, Bolivia and Venezuela. Driven by economic development concerns, these countries are pressuring the developed world to meet its commitments to both cut emissions and provide financial aid to poorer countries.

China, motivated by economic and political factors, seems to be happily filling the climate power vacuum created by the U.S. exit.

In 2017, China voiced disappointment over the first U.S. withdrawal. It maintained its climate commitments and pledged to contribute more in climate finance to other developing countries than the U.S. had committed to – US$3.1 billion compared with $3 billion.

This time around, China is using leadership on climate change in ways that fit its broader strategy of gaining influence and economic power by supporting economic growth and cooperation in developing countries. Through its Belt and Road Initiative, China has scaled up renewable energy exports and development in other countries, such as investing in solar power in Egypt and wind energy development in Ethiopia.

While China is still the world’s largest coal consumer, it has aggressively pursued investments in renewable energy at home, including solar, wind and electrification. In 2024, about half the renewable energy capacity built worldwide was in China.

While it missed the deadline to submit its climate pledge due this year, China has a goal of peaking its emissions before 2030 and then dropping to net-zero emissions by 2060. It is continuing major investments in renewable energy, both for its own use and for export. The U.S. government, in contrast, is cutting its support for wind and solar power. China also just expanded its carbon market to encourage emissions cuts in the cement, steel and aluminum sectors.

The British government has also ratcheted up its climate commitments as it seeks to become a clean energy superpower. In 2025, it pledged to cut emissions 77% by 2035 compared with 1990 levels. Its new pledge is also more transparent and specific than in the past, with details on how specific sectors, such as power, transportation, construction and agriculture, will cut emissions. And it contains stronger commitments to provide funding to help developing countries grow more sustainably.

In terms of corporate leadership, while many American businesses are being quieter about their efforts, in order to avoid sparking the ire of the Trump administration, most appear to be continuing on a green path – despite the lack of federal support and diminished rules.

USA Today and Statista’s “America’s Climate Leader List” includes about 500 large companies that have reduced their carbon intensity – carbon emissions divided by revenue – by 3% from the previous year. The data shows that the list is growing, up from about 400 in 2023.

What to watch at the 2025 climate talks

The Paris Agreement isn’t going anywhere. Given the agreement’s design, with each country voluntarily setting its own goals, the U.S. never had the power to drive it into obsolescence.

The question is if developed and developing country leaders alike can navigate two pressing needs – economic growth and ecological sustainability – without compromising their leadership on climate change.

This year’s U.N. climate conference in Brazil, COP30, will show how countries intend to move forward and, importantly, who will lead the way.

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I’m so excited that I’m writing today’s newsletter from my beloved hometown on the Mediterranean coast north of Barcelona.

I truly needed this break, and I’m enjoying every second I spend with my family… even waking up earlier than anyone to still send you these newsletters that are so important to me.

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Here we go, much love from the place that saw me grow up into my nerdy self!

There was a time when I believed research spoke for itself.

If the science was rigorous, if the paper was peer-reviewed, then surely someone outside the academic circles, somewhere, would recognize its value.

It wasn’t until I started framing my work through the lens of why it mattered that everything changed.

That’s when:

More people cared.
Funders listened.
Collaborators reached out.
And I finally felt aligned with the reason I became a scientist in the first place.

Let’s talk about how this works and how you can apply it to your own work.

Why framing matters more than you think

Imagine you’re working on soil microbe interactions. Important stuff. But say your abstract starts with:
“Species-specific microbial consortia mediate nitrogen flux under fluctuating precipitation scenarios.”

Now imagine you reframe it like this:

“Farmers around the world are struggling to grow food in increasingly unpredictable weather. Our team discovered how the right microbes can help soil hold onto nutrients during droughts, boosting resilience for crops and communities.”

The science didn’t change.
The framing did.

You went from “technical niche” to “climate resilience and food security.”

That’s not spin. That’s storytelling rooted in purpose.

The 3-Part Shift: From Research → Relevance → Resonance

Here’s the simple framework I use with Climate Ages stories and with my students:

1. What is the research?
   State it clearly, without jargon.

2. Why does it matter to someone specific?
   Who benefits? What changes? What’s at stake?

3. How can you tell that story in human language?
   Use real-world comparisons. Ground it in emotions, not just data.

Example:

“We studied fossil reef isotopes to understand abrupt extinction events.”

“Ancient reefs reveal how fast oceans can change. Understanding past collapses can help protect coral reefs today.”

Real talk: This is also how you unlock funding

Funders don’t fund ideas. They fund outcomes.

If your grant proposal sounds like a technical exercise, it may get lost in the pile. But if you can clearly show:

What’s at stake
Who it helps
Why it matters now

…you’re speaking their language.

That’s how you go from “another proposal” to “an urgent opportunity to fund.”

Try this exercise today

Pick a project you’ve worked on recently. Now rewrite it using this template:

1. Problem: What issue does your research help solve?

2. Action: What did you do or discover?

3. Impact: Why does it matter in the real world?

Here’s another example:

Before:
“We assessed groundwater salinization trends in peri-urban aquifers under increased anthropogenic stressors.”

After:
“Millions rely on groundwater to drink, farm, and live. Our study shows how urban sprawl is quietly salting our water, and what can be done to protect it.”

Which one do you think a journalist, funder, or policymaker is more likely to engage with?

Exactly.

Science with purpose isn’t fluff. It’s strategy.

When you frame your research through the lens of purpose:

You clarify your message.
You build trust with non-scientists.
You create ripple effects beyond citations.

This is what we do at Climate Ages’ Outreach Lab every day:
help scientists like you connect the dots between curiosity, credibility, and change.

Because the world doesn’t need more research papers that never get translated to a lay audience.

It needs more scientists brave enough to say:
“This matters. And here’s why.”

Your turn: Today’s exercise

Pick one of your current or past projects.
Rewrite it using the Problem → Action → Impact method.
Then post it on LinkedIn, share it in your newsletter, or pitch it to a journalist.

Or just send it to me: I’d love to see how you reframe your science through purpose.

Bridge your Science with the World
It’s ready to listen.

—

See you next week,

— Sílvia Pienda-Munoz, PhD — Climate Ages’ Outreach Lab

P.S. One last note: I’m opening the first Outreach Lab cohort in mid-September.

It’s a program designed to help you build a profitable and scalable Science Newslettter that attracts collaborations, brings funding, and increases your impact as a scientist.

If that sounds like something you’d like to be part of, you can join the waitlist here.

There will be only 20 spots in the first cohort, and spots will fill quickly.

What if the English language—and by extension, the AI trained on it—encouraged us to make things more complicated than they need to be? A fascinating study from the University of Birmingham, highlighted by the World Economic Forum, explores how both humans and large language models like ChatGPT tend to prefer “adding” over “subtracting” when improving text. This subtle bias toward elaboration isn’t just a cognitive quirk—it’s a linguistic one. And it may be shaping how we write, translate, and communicate, often in ways we don’t notice. For language professionals, this raises an important question: when AI suggests edits, is it helping us clarify—or just adding noise?

A linguistic bias in the English language that leads us to ‘improve’ things by adding to them, rather than taking away, is so common that it is even ingrained in AI chatbots, a new study reveals.

Language related to the concept of ‘improvement’ is more closely aligned with addition, rather than subtraction. This can lead us to make decisions which can overcomplicate things we are trying to make better.

The study is published in Cognitive Science, by an international research team from the Universities of Birmingham, Glasgow, Potsdam, and Northumbria University.

Dr Bodo Winter, Associate Professor in Cognitive Linguistics at the University of Birmingham said: “Our study builds on existing research which has shown that when people seek to make improvements, they generally add things.

“We found that the same bias is deeply embedded in the English language. For example, the word ‘improve’ is closer in meaning to words like ‘add’ and ‘increase’ than to ‘subtract’ and ‘decrease’, so when somebody at a meeting says, ‘Does anybody have ideas for how we could improve this?,’ it will already, implicitly, contain a call for improving by adding rather than improving by subtracting.”

The research also finds that other verbs of change like ‘to change’, ‘to modify’, ‘to revise’ or ‘to enhance’ behave in a similar way, and if this linguistic addition bias is left unchecked, it can make things worse, rather than improve them. For example, improving by adding rather than subtracting can make bureaucracy become excessive.

This bias works in reverse as well. Addition-related words are more frequent and more positive in ‘improvement’ contexts rather than subtraction-related words, meaning this addition bias is found at multiple levels of English language structure and use.

The bias is so ingrained that even AI chatbots have it built in. The researchers asked GPT-3, the predecessor of ChatGPT, what it thought of the word ‘add’. It replied: “The word ‘add’ is a positive word. Adding something to something else usually makes it better. For example, if you add sugar to your coffee, it will probably taste better. If you add a new friend to your life, you will probably be happier.”

Dr Winter concludes: “The positive addition bias in the English language is something we should all be aware of. It can influence our decisions and mean we are pre-disposed to add more layers, more levels, more things when in fact we might actually benefit from removing or simplifying.

“Maybe next time we are asked at work, or in life, to come up with suggestions on how to make improvements, we should take a second to consider our choices for a bit longer.”