Category: writing

  • What we lose when AI replaces teachers

    What we lose when AI replaces teachers

    eSchool News is counting down the 10 most-read stories of 2025. Story #8 focuses on the debate around teachers vs. AI.

    Key points:

    A colleague of ours recently attended an AI training where the opening slide featured a list of all the ways AI can revolutionize our classrooms. Grading was listed at the top. Sure, AI can grade papers in mere seconds, but should it?

    As one of our students, Jane, stated: “It has a rubric and can quantify it. It has benchmarks. But that is not what actually goes into writing.” Our students recognize that AI cannot replace the empathy and deep understanding that recognizes the growth, effort, and development of their voice. What concerns us most about grading our students’ written work with AI is the transformation of their audience from human to robot.

    If we teach our students throughout their writing lives that what the grading robot says matters most, then we are teaching them that their audience doesn’t matter. As Wyatt, another student, put it: “If you can use AI to grade me, I can use AI to write.” NCTE, in its position statements for Generative AI, reminds us that writing is a human act, not a mechanical one. Reducing it to automated scores undermines its value and teaches students, like Wyatt and Jane, that the only time we write is for a grade. That is a future of teaching writing we hope to never see.

    We need to pause when tech companies tout AI as the grader of student writing. This isn’t a question of capability. AI can score essays. It can be calibrated to rubrics. It can, as Jane said, provide students with encouragement and feedback specific to their developing skills. And we have no doubt it has the potential to make a teacher’s grading life easier. But just because we can outsource some educational functions to technology doesn’t mean we should.

    It is bad enough how many students already see their teacher as their only audience. Or worse, when students are writing for teachers who see their written work strictly through the lens of a rubric, their audience is limited to the rubric. Even those options are better than writing for a bot. Instead, let’s question how often our students write to a broader audience of their peers, parents, community, or a panel of judges for a writing contest. We need to reengage with writing as a process and implement AI as a guide or aide rather than a judge with the last word on an essay score.

    Our best foot forward is to put AI in its place. The use of AI in the writing process is better served in the developing stages of writing. AI is excellent as a guide for brainstorming. It can help in a variety of ways when a student is struggling and looking for five alternatives to their current ending or an idea for a metaphor. And if you or your students like AI’s grading feature, they can paste their work into a bot for feedback prior to handing it in as a final draft.

    We need to recognize that there are grave consequences if we let a bot do all the grading. As teachers, we should recognize bot grading for what it is: automated education. We can and should leave the promises of hundreds of essays graded in an hour for the standardized test providers. Our classrooms are alive with people who have stories to tell, arguments to make, and research to conduct. We see our students beyond the raw data of their work. We recognize that the poem our student has written for their sick grandparent might be a little flawed, but it matters a whole lot to the person writing it and to the person they are writing it for. We see the excitement or determination in our students’ eyes when they’ve chosen a research topic that is important to them. They want their cause to be known and understood by others, not processed and graded by a bot.

    The adoption of AI into education should be conducted with caution. Many educators are experimenting with using AI tools in thoughtful and student-centered ways. In a recent article, David Cutler describes his experience using an AI-assisted platform to provide feedback on his students’ essays. While Cutler found the tool surprisingly accurate and helpful, the true value lies in the feedback being used as part of the revision process. As this article reinforces, the role of a teacher is not just to grade, but to support and guide learning. When used intentionally (and we emphasize, as in-process feedback) AI can enhance that learning, but the final word, and the relationship behind it, must still come from a human being.

    When we hand over grading to AI, we risk handing over something much bigger–our students’ belief that their words matter and deserve an audience. Our students don’t write to impress a rubric, they write to be heard. And when we replace the reader with a robot, we risk teaching our students that their voices only matter to the machine. We need to let AI support the writing process, not define the product. Let it offer ideas, not deliver grades. When we use it at the right moments and for the right reasons, it can make us better teachers and help our students grow. But let’s never confuse efficiency with empathy. Or algorithms with understanding.

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  • Launching my new book, Peak Higher Ed

    Launching my new book, Peak Higher Ed

    Greetings from the darkest week of the year in the northern hemisphere. As winter solstice draws nigh I’m preparing some end-of-year posts.  But first, this month Johns Hopkins University Press is publishing my new book, Peak Higher Ed: How to Survive the Looming Academic Crisis. and I’m very excited.  Go, little book!

    On Thursday we held a Future Trends Forum session officially launching Peak.  For fun we switched up the usual arrangement, as I became a guest, not the host, and friends Wesson Radomsky and Brent Anders ran the show.

    I wanted to write about it here to share the news, but also because this book grew out of a blog post.  Let me explain.

    A dozen years ago I was doing some environmental scanning and was struck by some data which surprised me.  In 2012-13 American higher ed enrollment had declined slightly and this got me thinking.  As far as I knew student numbers had always been rising, at least in my experience.  What might a reversal mean?  If this one year’s decline wasn’t an aberration, what might a further decrease look like? If higher ed was starting to slide down past its peak, what could happen to colleges and universities? I did some futures analysis then jotted down thoughts in a blog post.

    In the ways blogs worked back then, this post stirred some comments and interest.  I reflected and noodled at the idea, next writing up the idea as an Inside Higher Ed column. Josh Kim wrote a riposte which helped develop things further. People wrote me offline and approached me in person, worried about what post-peak academia might become. I developed peak higher ed into a future scenario and started presenting on it to in-person and virtual audiences, adjusting it as feedback rolled in, while continuing to blog about it as new data appeared.  For example, in 2018 I noted that enrollment was still declining, as per the peak model. Again, Josh Kim responded. In 2019 I turned in the book manuscript for Academia Next and it included a peak higher ed scenario chapter.  That book covered a lot of ground, dwelling on many trends and multiple scenarios.

    Time passed. I continued to track and analyze trends reshaping higher education, including those which formed the peak model. I bounced the idea around with many people (see the book’s acknowledgements). I published a second Hopkins book, Universities on Fire, which focused on one single trend, climate change.

    In 2023 I wanted to change from writing about a trend to focusing on a scenario at length and pitched a book proposal to Johns Hopkins University Press. In my offer I worked in many issues besides the core peak idea to see how they might intersect. The publisher agreed to a contract.  Being a pro-open person, I quickly blogged my proposal and plan in 2024 then set to work. I actually followed the plan closely, developing each chapter in the proposed sequence, and now the book is in the world.  As a lifelong science fiction fan, I’m delighted to have a trilogy in print.

    That’s quite a journey starting from a single blog post.

    Let me say a bit more about what the book contains.  The first two chapters sketch out the scenario in some detail, describing how enrollment and the number of post-secondary institutions rose, peak, and fell.  Next, “After the Consensus Shattered” examines that story in light of the call for “college for everyone,” which has fallen on hard time (as I blogged).  A major and open question is: what collective understanding of higher education will succeed it?

    The next three chapters engage with three major forces or problems in the world and how they might intersect with post-peak academia. “Automation Comes for the Campus” focuses on AI and ends up offering several scenarios for how that technology might impact colleges and universities.  “The Anthropocene Is Here, Ready or Not” addresses climate change, the subject of my previous book, and explores several ways higher ed might engage that enormous force.  Then “Academia and the Struggle for Humanity’s Future” picks up my hypermodern/demodern idea to ponder how post-secondary education could grapple with emerging ideologies of human progress.

    The book concludes with two visions of the academy’s next phase.  One, “Managed Descent,” imagines the sector continuing to slide down away from peak.  The last, “Climbing Back to Peak,” offers some ways by which academics might reverse course and transform our institutions.  We could, if we dream boldly and with care, realize “peak” as in “peak performance.”

    There’s a little website for the book, where you can learn more about it, and which includes ordering links from Amazon, Bookshop.org, and Johns Hopkins.  I’m so grateful to the many people who helped me realize this book.  And as always, I’d love to hear from readers.

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  • You can write a great essay. But can you tell a great story?

    You can write a great essay. But can you tell a great story?

    What’s the difference between academic writing and writing a news story? How different can they be?

    I started out as a student of political science, became a journalist and then taught at university. Having started as an academic, you’d think returning to academic writing would be a snap. But it wasn’t.

    As a journalist I’d been trained to say what needed to be said in as few words as possible. My writing needed to be easily read by anybody, regardless of their level of education and whether or not they read English as a first language. But academic writing is meant to impress. An essay is written by a student to impress a teacher, or a professor to impress colleagues or a tenure committee, or by a scientist or social scientist to impress a publisher.

    Academic essays, reports and studies are meant to be ready by peers: people at the same or higher education level, who are experts in the same field of specialization and read them in their offices and classes.

    News stories are meant to be read by anyone, sitting around the breakfast table as they munch on corn flakes.

    You’d think the academic writing would be harder, no?

    Telling stories

    Imagine talking to a group of friends about something that happened in school. You don’t have to keep explaining who you are talking about or what you are talking about. They know all your references. But try telling the same story to your parents or better yet, adults who don’t know your school or community. It is a bit frustrating, because they don’t know what a stickler for rules Mr. Jackson is, or why most people avoid the third floor bathroom, or how so-and-so was dating you-know-who’s brother on the down low. You know, all that stuff that you need to know to understand why what happened at school was so significant.

    It is much more difficult to tell a story when the person you are telling it to has no context. Moreover, when you write an essay or report you expect the person you are writing it for to read it. That’s their job. But no one is expected to read a news story.

    As the author, you need to entice readers to choose your story to read. And you need to keep their attention throughout the story, because they aren’t obligated to read it to the end. So the story can’t be boring or tedious to read. Each paragraph has to have something interesting in it. It needs to be a good story worth reading.

    I learned quickly as a journalist to read my stories out loud to myself. By doing so, I could hear when my writing was getting tired and dull. I could picture the person who is hearing the story fall asleep or walk away. When that happened I hit the delete button and started the paragraph again.

    I would rethink whether the information I had included was really needed. Did my reader need to know that piece of data to understand what was happening?

    Comparing academic and journalistic writing

    To see the difference between journalistic and academic writing it is useful to look at a news story that came off of a report.

    The news organization Vox published an article 17 December about a new report on poverty that was done by researchers at four California universities.

    This is how the report began:

    We study poverty minimization via direct transfers, framing this as a statistical learning problem while retaining the information constraints faced by real-world programs. Using nationally representative household consumption surveys from 23 countries that together account for 50% of the world’s poor, we estimate that reducing the poverty rate to 1% (from a baseline of 12% at the time of last survey) would cost $170B nominal per year.

    Would you choose to read that with your corn flakes?

    Here is how Vox reporter Sara Herschander begins the story:

    When it comes to fixing the world’s worst problems, it’s easy to pretend that we’re helpless.

    We tell ourselves that global poverty is just too big, too distant and too intractable an issue for us to solve. If the world could afford to solve it, or something like hunger, then surely somebody else would have done it already.

    But, it turns out, that’s simply not true. According to a new report by a group of anti-poverty researchers that uses AI tools to achieve unusually granular data of the picture on the ground, the price tag for completely ending extreme poverty would be just $318 billion per year.

    Writing that is clear and concise

    The researchers didn’t worry that most people wouldn’t understand the terms “poverty minimization”, “direct transfers”, “statistical learning problem” or “information constraints”.

    But try sticking those terms into a story you tell friends in the school hall and they’ll tune you out.

    There is another big difference between news stories and academic essays and reports. Journalists don’t footnote sources. That’s because you wouldn’t have footnotes in a story you tell out loud. Just try it.

    So instead, when a journalist needs to cite a source they write something like, “that’s according to data from the U.S. Census” or, “a recent study out of Harvard found that.” The journalist would likely hyperlink to the actual study for readers who might want to read it, as I did above for both the Vox article and the report. The idea is that the citation should be as short as possible and it should not break into the story.

    The real challenge for a journalist is that the average reader has a very short attention span. Any break in a story is like an exit door. It is the chance for the reader to leave that depressing story about poverty to go to a more uplifting story about football or Bad Bunny.

    The importance of revision

    That’s why journalists write several drafts of a story before it gets published. In the first draft they just try to get all the information they have onto a page. In the second draft, they think about whether the information is needed and start taking things out and adding in others they might have forgotten. In the third, they try to close all those exit doors — all the places in the story that are tedious.

    There are some tricks to doing this. It helps to round up or down numbers that have a lot of digits. A number like $1,569,345 is tedious to read. It takes 13 words to say it out loud. Instead, saying about $1.6 million will do the trick. That’s just five words out loud.

    And it helps to use analogies and metaphors people can recognize. In a story I once wrote about the volatility of the stock market (doesn’t that sound like a yawner?) I likened the stock chart to Bart Simpson’s hair. For a story about an old technology company that kept getting sold and resold, I likened it to a secondhand sofa not moldy enough to toss into a skip.

    But reaching for these analogies isn’t easy; it takes a little extra time and mental effort. In some ways journalists are translators. In general, translators take something in one language and turn it into another — from Japanese to English, for example. A journalist takes something from the language of the boring and tedious and obscure and turns it into the language of interesting and understandable.

    It’s kind of like a jigsaw puzzle. You start with a bunch of pieces that seem to make little sense, but if you put them together in the right way you get a clear picture from it. But sometimes to do that you have to keep moving the different pieces around and sometimes you find you have to undo an entire section because something just doesn’t fit.

    The result, when you are done, though, is pretty satisfying.

    Questions to consider:

    1. Why are news stories so different from essays?

    2. In what ways are journalists translators?

    3. What do you think makes a story interesting to read or hear?

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  • Let’s have no locks on learning

    Let’s have no locks on learning

    At News Decoder we believe that information should flow across borders and that you shouldn’t need money to access it. That’s why we’ve created resources for both young people and educators that are open access — they are free and available to anyone. It is our mission, after all, to inform, connect and empower young people to be engaged citizens and changemakers locally, nationally and globally.

    These free resources include articles, podcasts and videos that offer young people tips on writing and reporting and information they need to be media literate such as guides for fact-checking news stories and verifying the accuracy of information. We also make publicly accessible our Decoder Dialogues —  in which we gather young people from different countries together with experts to talk about an important topic. You can watch our latest Decoder Dialogue on how young people speak out and stand up that took place 2 December. 

    For a great read on how some places are addressing the problems of paywalls on academic and scientific research, check out Charissa Eggers recent article for News Decoder. 

    Below are a series of useful guides for student journalists or for anyone who wants to become a better storyteller. 

    And check out more of our open access learning resources. 

    https://news-decoder.com/top-tips-can-you-be-the-bearer-of-good-news/ 

     

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  • When the clock ticks

    When the clock ticks

    I always respond the same: Give me a deadline you feel you can comfortably meet and then I can put it on our publishing calendar. What I don’t want is for the person to give me an early date and then not be able to meet it.

    So, how does a reporter writing a story for News Decoder come up with that deadline? It comes down to “doability”. That means what it says: what you can do, what is feasible. In determining doability, it helps to look at the opposite: Something that isn’t doable.

    Some things are difficult.

    What makes something not doable? The idea for the story is great, but realistically you won’t be able to interview anyone for it. Wouldn’t it be great to do a story on Russian hackers? But do you know any Russian hackers or anyone who knows Russian hackers? What about a story on the wealthy people giving money to political campaigns? Again, do you know anyone or can you realistically reach anyone who would give you information about that?

    In assessing the doability of a story, the first question to ask, then, is where your information will come from. You might not need to know key sources personally, but you need a way to be able to reach them and a reason to feel confident that they will talk to you.

    The second criteria is your financial wherewithal. To find the information, will you need to travel to get it? Do you have the money and time to do that?

    Third, if the subject deals with an uncomfortable subject — sexual assault, race, abortion, religion or suicide, for example — do you have the emotional resolve to be able to ask people difficult questions about their experiences? Not everyone can do that. You need to be honest with yourself about your willingness to tackle such topics.

    Last, what other responsibilities do you have that might interfere? How much time do you have to work on the story? If you have classes to attend or a job, will you only have a few hours here and there? That needs to be part of your calculation on how long it will take you to do the story.

    Many editors want to see these criteria explained when you pitch the story. They want to know that you have a solid plan for getting the information you need and the interviews to humanize the story. They want to know that you also have the wherewithal to do it.

    Be conservative. That means never overpromise. If you think it will take 20 hours to do the story, allow for 30. If you think you will need to spend $100 on travel costs, budget twice that. If you think you can turn in a story by Friday, promise it for the following Wednesday.

    No reporter was ever fired for turning in a solid story early. But if you want more story assignments you need to always, always turn them in when you promise them.

    Questions to consider:

    1. Why are deadlines so important in journalism?

    2. What is one piece of advice the author provides for meeting deadlines?

    3. Did you ever have a deadline that was difficult to meet? How did you handle it?

     

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  • Cook up a news story

    Cook up a news story

    Writing is the easy part; everything that comes before that is what’s hard. 

    That’s what News Decoder founder Nelson Graves told us back in 2020. Five years later, with the prevalence of artificial intelligence, this seems more true, doesn’t it? After all, you can just tell AI to write you a story and it will comply. 

    But what’s the point of that? It is one thing if your grade depends on the completion of a paper, and your graduation depends on that grade. Or maybe you can make some money churning out AI-written copy for some website. We won’t argue ethics here. 

    The point of this article, which I am thinking up and typing up word by word with no AI involvement, is to explain why the process of writing is the point. Apple founder Steve Jobs is often quoted as saying the journey is the reward. 

    Graves told us that the best stories emerge from a process that involves doing things that many people find difficult: Introspection, questioning your assumptions and interviewing people. All that seems even more of a challenge these days when it is so easy to tune out your feelings and avoid human interactions by listening to loud music, playing video games or bingeing TV shows.

    Again, why do that when AI could spit it out for you?

    Gather your ingredients.

    Graves, who spent his career writing for the news service Reuters, reminded us that writing is easy once you have the raw goods. That made me think about cooking. 

    Why do people take cooking classes and watch cooking videos when you can buy ready-made meals at Aldi? I often spend an entire afternoon in the kitchen making soup or a stew only to have my family gobble it up in 10 minutes. 

    It is hard to put together a fancy meal at the last minute. But if you have gathered your ingredients — the chopped vegetables, marinated chicken, diced onions and minced garlic — it is easy to toss them into a frying pan where the magic happens. 

    The same goes for a news story. If you have done your research — gathered some data, a timeline of events and information and quotes from interviews — then you are all set to toss them onto a page where the magic happens. 

    Follow a recipe.

    Ask yourself: Why do people become journalists when typically they don’t make much money and often get trolled and harassed — or worse — for what they publish? Many believe in the idea of public service, but really, there is nothing that matches the feeling of having published a great story. 

    It is like the satisfaction you get when the forkful of food goes into your mouth and tastes exquisite and you know you made it. You don’t get that feeling if you bought it ready-made from Aldi.

    People who don’t cook think cooking is hard or painful or not worth the effort. The funny thing is that once someone follows a recipe and makes something really tasty, that often changes the way they think about cooking and they try another recipe another day.

    The writing process is like a recipe. There are common steps journalists often follow. They don’t just open a blank page and start writing. So here is a basic recipe you can follow for just about any news story.

    1. Decide what to cook: This is your story idea. You can start broad: I’m going to make pasta. Then narrow it down to: Maybe a lasagna? Narrow it further, maybe based on the ingredients you already have. I’m going to make a spinach lasagna. So with a story you might start with this: I’m going to do a story about climate change. Then you narrow it: Maybe a story about pollution. Then you narrow it further: How about the factories around me that pollute the air?

    2. Find your ingredients. There are statistics you can get. A law has been proposed. A community group is planning a protest. The industry is coming out with new emissions guidelines. Interviews with advocates and proponents and lawmakers. 

    3. Decide in what order the ingredients go into the pan. For a news story there’s the lead that entices the reader (when you sauté garlic in butter people come into the kitchen salivating). Then there is the meat (we actually call it that in journalism), layered with the other ingredients: quotes, data, relevant events.

    With food, the order things go in is the recipe. In journalism it is an outline. It is an important part of the process. Without a good outline you have a mess of information and you don’t know what to do with it. An outline gives you a clear path to follow. The recipe for your story. 

    4. Put the final touch on the dish. It might be parmesan cheese on top, or garlicky bread crumbs or a drizzle of olive oil or soy sauce. For an article you want to end with a “kicker”: a good quote that sums everything up, maybe. 

    Finesse the flavors.

    What if you get to the end and it isn’t as tasty as you hoped? With cooking you tinker. A little more garlic? More salt or pepper? Yikes! I forgot the mushrooms! 

    In journalism, when the story seems flat you might reach out to one more source or call back one you already interviewed to get a better quote. You might look for a better example to use by doing another news search. 

    This is the revision process. And unlike in cooking, when you revise a story you can move your ingredients around and reorganize your story. Often that makes all the difference. 

    In the end you will have created something good, from scratch. It is a great feeling, even if your family takes 10 minutes to eat that lasagna it took you an hour to make. Even if a reader spends 30 seconds reading that story it took you days to craft. 

    The satisfaction you will feel won’t go away. 

    Questions to consider:

    1. If writing is the easy part, what is the hard part of creating a news story?

    2. What does it mean that the journey is the reward?

    3. Can you think of something you have done from scratch that you could have bought ready-made?

     

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  • Can you picture your story on a big screen?

    Can you picture your story on a big screen?

    Some people would rather watch movies than read news articles.

    The thing is, an awful lot of movies came out of news articles. Consider the entire Fast & Furious movie franchise, starring Vin Diesel and my personal movie favorite Michelle Rodriguez (shout out!). It revolves around people who race souped up cars on city streets.

    The idea of the first movie started with an article by journalist Ken Li, after he saw someone steal a car in New York and that spurred him to investigate the underground world of street racing. Someone at Universal Studios saw the article and bought the rights to it. 

    Or consider the Tom Cruise movie Top Gun, about a cocky U.S. Navy pilot. The idea for that came from a story in California magazine about Navy pilots.

    How can all this help an aspiring journalist? Well, thinking about your news story as the movie that might be commissioned from it is a way of seeing the story. So how do you go about doing that?

    Visualize your story

    First, think of the characters in your story. Who are the central actors involved? Who is the Vin Diesel or Tom Cruise in your story? 

    Who does the problem you are exploring affect? Who is causing it or standing in the way of solutions? Who are the people trying to solve or mitigate the problem? In journalism, the basic story structure is Who, What, Where, When and Why. The characters are the Who of the story. 

    The most compelling movies (and news stories) revolve around conflict: What are the stakes? In Fast & Furious, one of the main conflicts is the role of Brian O’Connor, who starts out as an FBI agent investigating the car racers and then becomes loyal to them. 

    Movie scripts revolve around turning points: What could change the course? What steps are being taken to solve or mitigate the problem you are exploring? What are people or corporations or governments or organizations doing that could worsen the situation? This is the What of the story. 

    Then think about the setting: Where is the crisis playing out? The original Fast & Furious took place in Los Angeles. Top Gun took place at a naval base in San Diego, California. This is the Where of the story. 

    Finally, what drives your story is the motivation of the characters: Why do they take the actions they do? 

    In Top Gun, Tom Cruise’s character is motivated by the death of his friend Goose to be the best pilot he can be. In Fast & Furious, Vin Diesel is motivated by the death of Michelle Rodriguez’s character to seek justice. 

    Actions and motivations

    Death is a common motivation in movies — the killing of John Wick’s dog triggered one of the most successful movie franchises out there. But for non-fiction news stories, there can be all kinds of motivations: parents wanting to get their kids into good schools, communities wanting to fight crime in their neighborhoods, governments wanting to end homelessness. 

    In news stories this is the Why of the story. Why does some corporation build a plant in your community? Why does some NGO oppose a development proposal? What’s their reason and motivation?

    So now try this: Think of a problem around you that you want to explore. It could be about anything from climate change, to mental health or inequities in sports or education. Start by noting down the Who (actors), What (what’s at stake), When, Where (setting) and Why (the motivations of the characters). Then turn this into a few paragraphs as if you’re writing for a news site. 

    Start with a hook: It should be something interesting or important. Why is this a big story? Why should people care? Then summarize in one paragraph the whole story. What’s the overall problem? Where is it happening and when, how did it start, what is causing it and who is it affecting? 

    Next, slowly work through each of those elements — the who, what, where, when, how and why. There is the meat of your story. Finally, talk about what’s next. What are the solutions or mitigations happening or proposed?

    Who knows? You might get your story published and down the line a Hollywood or Bollywood producer calls you up. Now, isn’t that motivation to write a news story? Just make sure you have a good agent.

    Questions to consider:

    1. How can seeing your story as a movie help you report and write it?

    2. If your life played out as a movie, what would be the central theme?

    3. Think about the most important thing you are doing these days. What motivates you to do it?

     

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  • Why have someone edit your story?

    Why have someone edit your story?

    Redundancy: Did you repeat anything unnecessarily?

    Accuracy: Did you make any factual mistakes or is anything misleading and can be read in a number of different ways?

    Sourcing: Were you able to show where your information came from and did you get the information from credible sources?

    Balance: Did you recognize multiple and opposing viewpoints or is the story one-sided and preachy?

    Organization: Did you bury the most interesting or important thing way down into your story? Did you wait too long to quote someone?

    Paragraphing: Are your paragraphs way too long? Long paragraphs are daunting to read, so try breaking them up. A paragraph can be a single sentence.

    Language: Is the story full of jargon normal people wouldn’t understand or long words only highly-educated people would know?

    Complexity: Is your story bogged down by too much information that isn’t really necessary?

    Clarity: Can a normal person understand the story on a quick read or is it confusing in any way?

    The editor’s role

    Ultimately the editor’s job is to make the story clear and readable. And both those things are hard to spot when you are the writer.

    Sometimes reporter balk at the suggestions editors make or the changes they insist must be done. When you have taken a lot of time and effort to report a story and have carefully worded and reworded your article it hurts to learn that it isn’t finished or that the editor thinks there are problems with it.

    But journalism is a collaborative process. It’s your story but it is also the editors story and the publication’s story. Your name will be on it — we call that the byline — but it will affect the publication’s reputation and that of the editor. Editors can find themselves fired or suspended if they publish a story that should not have been published. That’s the negative side of it.

    On the positive side, most editors genuinely want to make the story better — clearer, more powerful, a better overall read. And isn’t that what you want too? Over the course of my career, editors have saved me time and again by spotting mistakes I had inadvertently made. They have strengthened my writing and made me a better writer.

    Now if an editor suggests or insists on a change you really think isn’t necessary or will harm your story then fight against that. But do so respectfully and professionally.

    Ultimately the process isn’t meant to be fair. The editor has the final say. But if you can make a strong case and if you can show your editors why you care so much, chances are they will yield. Often this becomes a negotiation to find a way to word the material that satisfies both of you. But pick your battles carefully. No editor wants to work with a writer who fights every change or suggestion.

    A good partnership between a journalist and editor will help you write a great story and help ensure it stands up to the scrutiny of your audience.

     

    QUESTIONS TO CONSIDER:

    1. What is one way an editor can improve your story?

    2. If an editor wants change a story in a way you don’t like, what should you do?

    3. What traits do you have that would make you a good editor?

     

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  • How to use quotes in a story

    How to use quotes in a story

    Journalists talk to people. It is an important way to get information, at a time when many people allow artificial intelligence to do that for them. Facts and figures and things you find on the internet or in documents tell only part of a story. 

    How many things have you told a friend or family member that you wouldn’t want to put down in writing? How many times have you been in a discussion with a group of people who had different takes on something that you all experienced? 

    Haven’t you ever had a surprising epiphany in the middle of a conversation?

    By talking to multiple people who have different perspectives and comparing those thoughts or comments or stories with facts and figures and reports, journalists try to get at the truth of something that happened or is happening. They are also able to instill into an article or podcast the passion and emotion missing from government or academic reports. 

    But once you are ready to write your story, how do you use the information you get from interviews and what do you do with those quotes? 

    First, do some interviews.

    Let’s understand why you even include quotes in a story. One, because it humanizes a story that would otherwise be a tedious read. 

    You could give me a whole argument of why pollution in a river is bad. But it hits me if someone says, “The last time I went swimming, I came out with hives all over my body,” or, “The river is right out our door, but I have to drive my son across the city to the public pool to swim because the river is filthy.”

    Second, including quotes from interviews you did yourself shows your readers or listeners that you didn’t just slap together the story. That gives you credibility in a world where people won’t trust much of what they read. 

    Now, you won’t get that if, instead of interviewing people yourself, you just grab quotes from articles in other publications. When you do that, the opposite happens. You give readers a reason not to trust you, because you are simply reprinting what you read elsewhere. That comes across as lazy and careless. 

    The same is true if you take quotes off press releases issued by some corporation, organization or politician. Worse, because if you don’t tell readers that the quote came from a press release you mislead them. You make it seem as if you spoke to someone when you didn’t. And often, public relations people are allowed to just make up quotes in those press releases; the CEOs or politicians never actually said them the things they are quoted as saying. 

    Bottom line: Avoid using quotes you didn’t get yourself. 

    Using quotes in a story

    So let’s say you did an interview or two. How do you use the quotes from that interview?

    First, understand that quotes are sacrosanct. Once you have quote marks around something someone says, don’t change what is inside those quote marks. You are telling your reader: This person said this exactly. 

    If the quote includes a lot of unnecessary words, what we call blah blah blah, you can’t just delete that within the quote marks. Some people use ellipses (…) to connect the important and relevant parts of the quote without bogging it down with the blah blah blah. Others just take part of the quote. We call that a partial quote. 

    Now, that’s a style preference. Personally, I hate to do that because when you do you expect your reader to trust you. They might instead think you are withholding good information because you don’t agree with it. You risk losing that important credibility you gained by doing the interview in the first place. 

    Instead, I paraphrase. That means that you take the quote marks off the quote and instead, you attribute it. That means that you tag the information with so-and-so said. 

    Not everyone has the the gift of gab.

    You might end up paraphrasing a lot in a story if the people you interviewed don’t have the gift of speech and are nervous and stumble on words or are really boring to talk to, but have good information to give you. You can get great information from boring people! 

    Remember your role. You are talking to these people because your readers or listeners don’t have access to them or wouldn’t want to talk to them. I’ve done hours long interviews where two quotes end up in the story. Those two quotes made it worthwhile but my readers would never have wanted to sit through those painful interviews. 

    And unless you can count on a readership of super-educated people who have great attention spans, keep those quotes short. Really, a quote can be three words: “I felt awful!” she said. 

    If a quote is long to the point where it becomes tedious, paraphrase. When you paraphrase, you can cut out the gobbledygook and even change words as long as you don’t change the meaning of what the person said. 

    That’s a never. 

    Never ever change the meaning of what someone says. If you must change any words from statements in an interview, you need to really understand what the person said and even more so, what the person meant to say. 

    To misquote someone word for word

    I’ve known journalists accused of misquoting someone when they had the statement word for word on a recording. The person simply couldn’t believe they would have said what they said, even though they said it. 

    Now you might think, great! The journalist caught the person. Some people call these “gotcha” moments. 

    But think about your role as a journalist. Isn’t it to get at a truth? And should you penalize people who maybe aren’t used to being interviewed and are nervous and might say things because their brains don’t really have time to work out their thoughts properly? People will feel compelled to impress you or say what they think you want them to say.

    The rule of thumb I go by is that I try to treat people the way I would want to be treated. I get nervous talking to people. I say things I wish I hadn’t said and don’t really mean. I’d be mortified if everything I said ended up in print in some widely read publication. In a class I once taught I caught a student texting on his phone and he told me he was posting what I had just said. That shut me up. 

    Meanwhile, just because someone says something, doesn’t make it true. There is no excuse for including inaccurate or misleading information in a story even if it is said by someone with a fancy title or a prestigious reputation. People can make mistakes, exaggerate and mislead. Quote marks aren’t a blank license to publish. 

    Quotes should pop out.

    Quote marks are like little neon borders around a piece of information. They should stand out. So avoid putting quote marks around basic facts like dates or times or an undisputed amount of money. Quotes should transmit emotion or opinions or ideas. Or as my friend and colleague Deidre Pike says, “Quote the memorable. Paraphrase the mundane.”

    But do you actually have to speak to someone to quote them in a story? A while back, I’d have said yes. But now so much communication is done by email or digital chats that it has become a standard form of dialogue. How many people hate talking on the phone now? Limiting yourself to only people you can talk to in person or by telephone or videoconferencing could limit the types of people you get, and the goal is to get the best information from the best people you can. 

    Transparency is important, though. Let your readers know that you interviewed the person over WhatsApp or LinkedIn or whatever form it took. (My disclosure: the quote I grabbed from Deidre Pike was from her response to a Linkedin inquiry I posted).

    But don’t do that as a default. You are less likely to get that great emotion and passion in a post than you would in person or the phone or on a Zoom call. So try for voice or in person interviews whenever you can. 

    Plus interviews are fun. That person-to-person direct communication builds a connection that you don’t get through instant messaging or email. Hearing someone burst out laughing is way better than an “LOL!” in a text. And while waiting for a message to drop you can’t tell if the person just got distracted because their dog jumped on their lap or the question troubles them and they are taking time to think. But if you are watching them, you can tell. 

    It is harder too to get those memorable anecdotes for a story that will bring it to life. And you can’t count on the uncomfortable silences that get people to open up. 

    Regardless of how you get your quotes, getting them is only the first step. Knowing how to use them in your story will make all the difference. 

    And you can quote me on that. 

     

    Questions to consider:

    1. How can a quote from an interview improve a story?

    2. Why would you paraphrase something someone says instead of quoting it directly?

    3. Why should double check information that an expert told you?

     

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  • What really shapes the future of AI in education?

    What really shapes the future of AI in education?

    This post originally appeared on the Christensen Institute’s blog and is reposted here with permission.

    Key points:

    A few weeks ago, MIT’s Media Lab put out a study on how AI affects the brain. The study ignited a firestorm of posts and comments on social media, given its provocative finding that students who relied on ChatGPT for writing tasks showed lower brain engagement on EEG scans, hinting that offloading thinking to AI can literally dull our neural activity. For anyone who has used AI, it’s not hard to see how AI systems can become learning crutches that encourage mental laziness.

    But I don’t think a simple “AI harms learning” conclusion tells the whole story. In this blog post (adapted from a recent series of posts I shared on LinkedIn), I want to add to the conversation by tackling the potential impact of AI in education from four angles. I’ll explore how AI’s unique adaptability can reshape rigid systems, how it both fights and fuels misinformation, how AI can be both good and bad depending on how it is used, and why its funding model may ultimately determine whether AI serves learners or short-circuits their growth.

    What if the most transformative aspect of AI for schools isn’t its intelligence, but its adaptability?

    Most technologies make us adjust to them. We have to learn how they work and adapt our behavior. Industrial machines, enterprise software, even a basic thermostat—they all come with instructions and patterns we need to learn and follow.

    Education highlights this dynamic in a different way. How does education’s “factory model” work when students don’t come to school as standardized raw inputs? In many ways, schools expect students to conform to the requirements of the system—show up on time, sharpen your pencil before class, sit quietly while the teacher is talking, raise your hand if you want to speak. Those social norms are expectations we place on students so that standardized education can work. But as anyone who has tried to manage a group of six-year-olds knows, a class of students is full of complicated humans who never fully conform to what the system expects. So, teachers serve as the malleable middle layer. They adapt standardized systems to make them work for real students. Without that human adaptability, the system would collapse.

    Same thing in manufacturing. Edgar Schein notes that engineers aim to design systems that run themselves. But operators know systems never work perfectly. Their job—and often their sense of professional identity—is about having the expertise to adapt and adjust when things inevitably go off-script. Human adaptability in the face of rigid systems keeps everything running.

    So, how does this relate to AI? AI breaks the mold of most machines and systems humans have designed and dealt with throughout history. It doesn’t just follow its algorithm and expect us to learn how to use it. It adapts to us, like how teachers or factory operators adapt to the realities of the world to compensate for the rigidity of standardized systems.

    You don’t need a coding background or a manual. You just speak to it. (I literally hit the voice-to-text button and talk to it like I’m explaining something to a person.) Messy, natural human language—the age-old human-to-human interface that our brains are wired to pick up on as infants—has become the interface for large language models. In other words, what makes today’s AI models amazing is their ability to use our interface, rather than asking us to learn theirs.

    For me, the early hype about “prompt engineering” never really made sense. It assumed that success with AI required becoming an AI whisperer who knew how to speak AI’s language. But in my experience, working well with AI is less about learning special ways to talk to AI and more about just being a clear communicator, just like a good teacher or a good manager.

    Now imagine this: what if AI becomes the new malleable middle layer across all kinds of systems? Not just a tool, but an adaptive bridge that makes other rigid, standardized systems work well together. If AI can make interoperability nearly frictionless—adapting to each system and context, rather than forcing people to adapt to it—that could be transformative. It’s not hard to see how this shift might ripple far beyond technology into how we organize institutions, deliver services, and design learning experiences.

    Consider two concrete examples of how this might transform schools. First, our current system heavily relies on the written word as the medium for assessing students’ learning. To be clear, writing is an important skill that students need to develop to help them navigate the world beyond school. Yet at the same time, schools’ heavy reliance on writing as the medium for demonstrating learning creates barriers for students with learning disabilities, neurodivergent learners, or English language learners—all of whom may have a deep understanding but struggle to express it through writing in English. AI could serve as that adaptive layer, allowing students to demonstrate their knowledge and receive feedback through speech, visual representations, or even their native language, while still ensuring rigorous assessment of their actual understanding.

    Second, it’s obvious that students don’t all learn at the same pace—yet we’ve forced learning to happen at a uniform timeline because individualized pacing quickly becomes completely unmanageable when teachers are on their own to cover material and provide feedback to their students. So instead, everyone spends the same number of weeks on each unit of content and then moves to the next course or grade level together, regardless of individual readiness. Here again, AI could serve as that adaptive layer for keeping track of students’ individual learning progressions and then serving up customized feedback, explanations, and practice opportunities based on students’ individual needs.

    Third, success in school isn’t just about academics—it’s about knowing how to navigate the system itself. Students need to know how to approach teachers for help, track announcements for tryouts and auditions, fill out paperwork for course selections, and advocate for themselves to get into the classes they want. These navigation skills become even more critical for college applications and financial aid. But there are huge inequities here because much of this knowledge comes from social capital—having parents or peers who already understand how the system works. AI could help level the playing field by serving as that adaptive coaching layer, guiding any student through the bureaucratic maze rather than expecting them to figure it out on their own or rely on family connections to decode the system.

    Can AI help solve the problem of misinformation?

    Most people I talk to are skeptical of the idea in this subhead—and understandably so.

    We’ve all seen the headlines: deep fakes, hallucinated facts, bots that churn out clickbait. AI, many argue, will supercharge misinformation, not solve it. Others worry that overreliance on AI could make people less critical and more passive, outsourcing their thinking instead of sharpening it.

    But what if that’s not the whole story?

    Here’s what gives me hope: AI’s ability to spot falsehoods and surface truth at scale might be one of its most powerful—and underappreciated—capabilities.

    First, consider what makes misinformation so destructive. It’s not just that people believe wrong facts. It’s that people build vastly different mental models of what’s true and real. They lose any shared basis for reasoning through disagreements. Once that happens, dialogue breaks down. Facts don’t matter because facts aren’t shared.

    Traditionally, countering misinformation has required human judgment and painstaking research, both time-consuming and limited in scale. But AI changes the equation.

    Unlike any single person, a large language model (LLM) can draw from an enormous base of facts, concepts, and contextual knowledge. LLMs know far more facts from their training data than any person can learn in a lifetime. And when paired with tools like a web browser or citation database, they can investigate claims, check sources, and explain discrepancies.

    Imagine reading a social media post and getting a sidebar summary—courtesy of AI—that flags misleading statistics, offers missing context, and links to credible sources. Not months later, not buried in the comments—instantly, as the content appears. The technology to do this already exists.

    Of course, AI is not perfect as a fact-checker. When large language models generate text, they aren’t producing precise queries of facts; they’re making probabilistic guesses at what the right response should be based on their training, and sometimes those guesses are wrong. (Just like human experts, they also generate answers by drawing on their expertise, and they sometimes get things wrong.) AI also has its own blind spots and biases based on the biases it inherits from its training data. 

    But in many ways, both hallucinations and biases in AI are easier to detect and address than the false statements and biases that come from millions of human minds across the internet. AI’s decision rules can be audited. Its output can be tested. Its propensity to hallucinate can be curtailed. That makes it a promising foundation for improving trust, at least compared to the murky, decentralized mess of misinformation we’re living in now.

    This doesn’t mean AI will eliminate misinformation. But it could dramatically increase the accessibility of accurate information, and reduce the friction it takes to verify what’s true. Of course, most platforms don’t yet include built-in AI fact-checking, and even if they did, that approach would raise important concerns. Do we trust the sources that those companies prioritize? The rules their systems follow? The incentives that guide how their tools are designed? But beyond questions of trust, there’s a deeper concern: when AI passively flags errors or supplies corrections, it risks turning users into passive recipients of “answers” rather than active seekers of truth. Learning requires effort. It’s not just about having the right information—it’s about asking good questions, thinking critically, and grappling with ideas. That’s why I think one of the most important things to teach young people about how to use AI is to treat it as a tool for interrogating the information and ideas they encounter, both online and from AI itself. Just like we teach students to proofread their writing or double-check their math, we should help them develop habits of mind that use AI to spark their own inquiry—to question claims, explore perspectives, and dig deeper into the truth. 

    Still, this focuses on just one side of the story. As powerful as AI may be for fact-checking, it will inevitably be used to generate deepfakes and spin persuasive falsehoods.

    AI isn’t just good or bad—it’s both. The future of education depends on how we use it.

    Much of the commentary around AI takes a strong stance: either it’s an incredible force for progress or it’s a terrifying threat to humanity. These bold perspectives make for compelling headlines and persuasive arguments. But in reality, the world is messy. And most transformative innovations—AI included—cut both ways.

    History is full of examples of technologies that have advanced society in profound ways while also creating new risks and challenges. The Industrial Revolution made it possible to mass-produce goods that have dramatically improved the quality of life for billions. It has also fueled pollution and environmental degradation. The internet connects communities, opens access to knowledge, and accelerates scientific progress—but it also fuels misinformation, addiction, and division. Nuclear energy can power cities—or obliterate them.

    AI is no different. It will do amazing things. It will do terrible things. The question isn’t whether AI will be good or bad for humanity—it’s how the choices of its users and developers will determine the directions it takes. 

    Because I work in education, I’ve been especially focused on the impact of AI on learning. AI can make learning more engaging, more personalized, and more accessible. It can explain concepts in multiple ways, adapt to your level, provide feedback, generate practice exercises, or summarize key points. It’s like having a teaching assistant on demand to accelerate your learning.

    But it can also short-circuit the learning process. Why wrestle with a hard problem when AI will just give you the answer? Why wrestle with an idea when you can ask AI to write the essay for you? And even when students have every intention of learning, AI can create the illusion of learning while leaving understanding shallow.

    This double-edged dynamic isn’t limited to learning. It’s also apparent in the world of work. AI is already making it easier for individuals to take on entrepreneurial projects that would have previously required whole teams. A startup no longer needs to hire a designer to create its logo, a marketer to build its brand assets, or an editor to write its press releases. In the near future, you may not even need to know how to code to build a software product. AI can help individuals turn ideas into action with far fewer barriers. And for those who feel overwhelmed by the idea of starting something new, AI can coach them through it, step by step. We may be on the front end of a boom in entrepreneurship unlocked by AI.

    At the same time, however, AI is displacing many of the entry-level knowledge jobs that people have historically relied on to get their careers started. Tasks like drafting memos, doing basic research, or managing spreadsheets—once done by junior staff—can increasingly be handled by AI. That shift is making it harder for new graduates to break into the workforce and develop their skills on the job.

    One way to mitigate these challenges is to build AI tools that are designed to support learning, not circumvent it. For example, Khan Academy’s Khanmigo helps students think critically about the material they’re learning rather than just giving them answers. It encourages ideation, offers feedback, and prompts deeper understanding—serving as a thoughtful coach, not a shortcut. But the deeper issue AI brings into focus is that our education system often treats learning as a means to an end—a set of hoops to jump through on the way to a diploma. To truly prepare students for a world shaped by AI, we need to rethink that approach. First, we should focus less on teaching only the skills AI can already do well. And second, we should make learning more about pursuing goals students care about—goals that require curiosity, critical thinking, and perseverance. Rather than training students to follow a prescribed path, we should be helping them learn how to chart their own. That’s especially important in a world where career paths are becoming less predictable, and opportunities often require the kind of initiative and adaptability we associate with entrepreneurs.

    In short, AI is just the latest technological double-edged sword. It can support learning, or short-circuit it. Boost entrepreneurship—or displace entry-level jobs. The key isn’t to declare AI good or bad, but to recognize that it’s both, and then to be intentional about how we shape its trajectory. 

    That trajectory won’t be determined by technical capabilities alone. Who pays for AI, and what they pay it to do, will influence whether it evolves to support human learning, expertise, and connection, or to exploit our attention, take our jobs, and replace our relationships.

    What actually determines whether AI helps or harms?

    When people talk about the opportunities and risks of artificial intelligence, the conversation tends to focus on the technology’s capabilities—what it might be able to do, what it might replace, what breakthroughs lie ahead. But just focusing on what the technology does—both good and bad—doesn’t tell the whole story. The business model behind a technology influences how it evolves.

    For example, when advertisers are the paying customer, as they are for many social media platforms, products tend to evolve to maximize user engagement and time-on-platform. That’s how we ended up with doomscrolling—endless content feeds optimized to occupy our attention so companies can show us more ads, often at the expense of our well-being.

    That incentive could be particularly dangerous with AI. If you combine superhuman persuasion tools with an incentive to monopolize users’ attention, the results will be deeply manipulative. And this gets at a concern my colleague Julia Freeland Fisher has been raising: What happens if AI systems start to displace human connection? If AI becomes your go-to for friendship or emotional support, it risks crowding out the real relationships in your life.

    Whether or not AI ends up undermining human relationships depends a lot on how it’s paid for. An AI built to hold your attention and keep you coming back might try to be your best friend. But an AI built to help you solve problems in the real world will behave differently. That kind of AI might say, “Hey, we’ve been talking for a while—why not go try out some of the things we’ve discussed?” or “Sounds like it’s time to take a break and connect with someone you care about.”

    Some decisions made by the major AI companies seem encouraging. Sam Altman, OpenAI’s CEO, has said that adopting ads would be a last resort. “I’m not saying OpenAI would never consider ads, but I don’t like them in general, and I think that ads-plus-AI is sort of uniquely unsettling to me.” Instead, most AI developers like OpenAI and Anthropic have turned to user subscriptions, an incentive structure that doesn’t steer as hard toward addictiveness. OpenAI is also exploring AI-centric hardware as a business model—another experiment that seems more promising for user wellbeing.

    So far, we’ve been talking about the directions AI will take as companies develop their technologies for individual consumers, but there’s another angle worth considering: how AI gets adopted into the workplace. One of the big concerns is that AI will be used to replace people, not necessarily because it does the job better, but because it’s cheaper. That decision often comes down to incentives. Right now, businesses pay a lot in payroll taxes and benefits for every employee, but they get tax breaks when they invest in software and machines. So, from a purely financial standpoint, replacing people with technology can look like a smart move. In the book, The Once and Future Worker, Oren Cass discusses this problem and suggests flipping that script—taxing capital more and labor less—so companies aren’t nudged toward cutting jobs just to save money. That change wouldn’t stop companies from using AI, but it would encourage them to deploy it in ways that complement, rather than replace, human workers.

    Currently, while AI companies operate without sustainable business models, they’re buoyed by investor funding. Investors are willing to bankroll companies with little or no revenue today because they see the potential for massive profits in the future. But that investor model creates pressure to grow rapidly and acquire as many users as possible, since scale is often a key metric of success in venture-backed tech. That drive for rapid growth can push companies to prioritize user acquisition over thoughtful product development, potentially at the expense of safety, ethics, or long-term consequences. 

    Given these realities, what can parents and educators do? First, they can be discerning customers. There are many AI tools available, and the choices they make matter. Rather than simply opting for what’s most entertaining or immediately useful, they can support companies whose business models and design choices reflect a concern for users’ well-being and societal impact.

    Second, they can be vocal. Journalists, educators, and parents all have platforms—whether formal or informal—to raise questions, share concerns, and express what they hope to see from AI companies. Public dialogue helps shape media narratives, which in turn shape both market forces and policy decisions.

    Third, they can advocate for smart, balanced regulation. As I noted above, AI shouldn’t be regulated as if it’s either all good or all bad. But reasonable guardrails can ensure that AI is developed and used in ways that serve the public good. Just as the customers and investors in a company’s value network influence its priorities, so too can policymakers play a constructive role as value network actors by creating smart policies that promote general welfare when market incentives fall short.

    In sum, a company’s value network—who its investors are, who pays for its products, and what they hire those products to do—determines what companies optimize for. And in AI, that choice might shape not just how the technology evolves, but how it impacts our lives, our relationships, and our society.

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