Sixteen tables, two seats to a table, all in rows, face front “because that’s where the instruction is coming from,” he said.

About the only technology in the room: small handheld whiteboards, dry-erase pens and small stacks of index cards. The walls are almost entirely bare. And phones are out of the question, stowed in backpacks before class.

It’s intentional, said Harvard, who teaches Advanced Placement Psychology at James Clemens High School in Madison, Ala., a suburb of Huntsville.

Over the past decade, he has become something of an expert in focus, memory, forgetting and distraction.

Harvard has put these principles into his first book, published last week, titled, appropriately, . 

Harvard hopes the book will offer practical advice to teachers on how to use the principles of cognitive science to create better learning environments.

The time is right for a new book about attention, said , a professor of English at the City University of New York and founding director of CUNY’s Futures Initiative. She said she’s excited to see Harvard’s work.

Davidson noted several indicators of rising inattention, from falling reading scores to the growth of media misinformation and the higher prevalence of young people who say they’re with traditional education. 

“I think people are really seeing that what it means to pay attention is important,” said Davidson, who wrote 2011’s . 

Harvard mostly focuses on more intentional teaching methods that reduce distractions and help students manage the vast amount of content they’re called upon to remember —  often called “.”

These ideas are decidedly not on tap in most teacher preparation programs, said Harvard, who earned his master’s degree in education in 2006. His coursework contained “nothing on cognition — there was nothing on the brain, nothing on how we learn.”

‘Why don’t I already know about this?’

It wasn’t until 2016, a decade after graduate school, that Harvard happened upon the now-defunct Twitter account “The Learning Scientists.” In plain language, educational psychologists from around the world laid out the basics of cognitive science for educators. 

Harvard was gobsmacked. Instead of just shooting in the dark, he finally saw research on the effectiveness of various learning strategies. 

He found himself instantly hooked and soon for the group. That led to his own website, which eventually became the popular blog .

Nearly a decade later, he’s traveling the world, speaking at conferences about strategies that affect students’ ability to channel ideas into long-term memory. He’s lost count of how many times he’s had to inform audiences that — humans can’t consciously focus on more than one thing at a time.

Harvard subscribes to something he calls the “SAR method,” an accessible way for students and teachers to think about memory. When they’re about to start a lesson, he tells students that memory follows a three-step process: Sense, Attend and Rehearse. 

“You can hear your teacher,” he said. “You can see your teacher. You can see the board. You can sense it. But are you attending to it? Are you paying attention to it, or are there things getting in your way? Are you trying to multitask? Is the person sitting next to you talking?”

Once a student attends to the material, the rehearsal happens. That’s perhaps the most important and tricky part. In the book, he likens it to an athlete’s ability to learn a new routine. If he or she doesn’t rehearse before the big game, he writes, “that would not be a good recipe for success on the playing field.”

Rehearsing in the classroom can take the form of a multiple-choice quiz, a discussion or a project. The key is to access the material from memory and use it appropriately.

Accordingly, he begins many classes by simply asking students to review what came the day, the week or even the month before. Retrieving those memories, he said, makes them more likely to be there the next time the brain goes looking for them.

Another principle he employs is “wait time.” When most teachers ask a question, they’ll settle for the first student with her hand up. But Harvard adds a step, ordering students to retrieve their handheld whiteboard. Before anyone can answer out loud, everyone must attempt an answer in writing.

“Now they’re committed to thinking,” he said. “They’re committed to writing something down. It seems like such a simple thing, but when you make the students do that, you give them time to think.”

As they’re studying, he’ll often give students a kind of slow-motion, three-stage assessment he calls “Brain-Book-Buddy” to offer a more honest take on what they actually know.

In the first assessment, they answer a series of questions from memory. Then they fill in the answers they couldn’t remember with the help of their notes. In the final test, they can talk to classmates.

“They end up getting all the right answers, but they’re also acutely aware of what they actually knew, what they knew with their notebook, and what they had to ask their buddies, their peers, about,” he said. “It’s an ongoing conversation of them thinking about their thinking.”

‘Attention Contagion’

Lately Harvard has been evangelizing most eagerly about an emerging topic in cognitive science known as “.” Only a handful of small-scale studies exist on the topic, but Harvard says the evidence is compelling.

In the research, students pose as attentive or non-attentive classmates, and researchers judge how well actual subjects attend to lessons in their presence — how many notes they take and their performance on post-lesson quizzes. The results suggest that seatmates’ behaviors have a profound effect: When a student is surrounded by inattentive peers, the behaviors are contagious. It works the other way as well: If a student is surrounded by peers who are visibly paying attention, they’re more attentive. 

had undergraduates watch a video lecture with a “classmate” posing as someone who either seemed attentive — leaning forward and taking notes — or slouched, shifting his gaze, glancing at the clock and taking infrequent notes. Researchers found that being seated behind these classmates had a profound effect: Subjects sitting near attentive students took significantly more notes and rated themselves as being on task. They also scored more than five points higher on a multiple-choice quiz.

Other studies have replayed the dynamic, with similar results. The findings even hold true for students observing one another in a Zoom-like virtual environment, where all that’s visible is a student’s face staring into a webcam.

In other words, Harvard notes, attention and inattention can actually pass through the Internet.

He considers the findings especially resonant because the “contagion” doesn’t come from obviously bad behavior like yelling, interrupting a teacher or staring at a phone. It’s stuff that he and most other teachers would typically let slide.

“They’re just slouching in their chair,” he said. “They’re just not taking notes. They’re gazing out the window.”

What the studies show is that attention operates by a kind of quiet osmosis, in some cases literally felt but not seen.

, the researcher who has pioneered this work, emphasized the “non-distracting” nature of the inattentiveness in his studies, noting that it’s “driven by more than just peer distraction.” Peers can detect these inattentiveness cues, he told The 74, even via tiny changes in the case of the online environment, suggesting that students “pay attention to their peers on webcam — even when the video thumbnails are quite small.”

More data needed

In an email, Forrin cautioned that attention contagion ”has not yet been studied in real classrooms,” only in laboratory settings with video lecturers. But he said he’s confident that attention and inattention “can spread between students during lectures,” and that this spread affects learning. Students “are attuned to their peers’ motivation to learn” and pay more attention when they infer that others have strong learning goals. They pay less attention when they sense weak or no goals. 

He suggested that teachers do their best to cultivate these goals in their students. They should also let students choose their own seats so they’re not consistently sitting near inattentive peers.

But he said more data are needed to determine whether these phenomena occur in real classrooms, especially with live teachers and different levels of student motivation.

Davidson, the CUNY scholar, said research on topics similar to attention contagion go back all the way to , who at the turn of the 20th century was studying the social aspects of “vivid” thoughts, distraction and focus. More recently, she noted, the psychologist Danie Kahneman, who helped establish what has become behavioral economics, studied .

And of course TV producers who pioneered the “canned laughter” of laugh tracks on early TV knew that suggestions of an engaged audience make viewers respond in kind. 

But perhaps the greatest experts in attention contagion, Davidson said, are stand-up comedians — she interviewed several for her 2011 book, and they told her that visibly bored audience members are “the kiss of death” in live performance. “People fall asleep in the front row, and pretty soon they’re falling asleep in the whole theater,” she said.

Harvard, for his part, is convinced that attention contagion in the classroom is real — and he tells students about the research.

“It’s powerful for students to hear that simply being inattentive can distract someone else from learning,” he said.

More broadly, he said, cognitive psychology has simplified his approach to teaching, allowing him to focus on proven strategies that are neither traditional nor progressive. 

The most cynical person, he said, would probably say his classroom is “too traditional. But I’m not thinking, ‘Do I want a traditional or a progressive classroom?’ When I designed it, I’m thinking, ‘How can I put my students in the best situation where they can pay attention to what they need to pay attention [to] and be distracted the least?’ That’s everything that I’m thinking about, and nothing else.”

And here’s a twist: The same core difficulties are bedeviling teachers too.

The findings, contained in a new working paper, are believed to be the first to identify brain changes as an explanation for why students have suffered, both inside and outside the classroom, since the pandemic drove millions out of the classroom. 

, a Harvard University psychologist who studies the effects of stress on executive functions and who is the study’s lead author, said the new findings offer the first evidence to help us “understand the ‘why’” of the pandemic downturn — “what is actually causing all these issues that we’re seeing and talking about in the news.”

The paper, from the educational assessment and services company“, examines the cognitive skills of students nationwide and finds that, simply put, over the past several years, kids’ famously ever-changing brains have changed for the worse. 

Since the pandemic’s onset, students across all ages and economic levels have begun to demonstrate weaker memory and “flexible thinking” skills — those represent the mental bandwidth needed for multitasking, shifting from one activity to another and juggling the day’s demands. But for a few groups, such as younger and lower-income children, the changes have been more profound.

They also show that their teachers’ brains are weaker in almost identical ways, which could help explain high rates of frustration and burnout. They suggest school districts have their work cut out for them if they want to keep their best employees on the payroll and returning to the classroom each fall. 

Understanding the ‘why’ of pandemic downturn

The data come from a large, widely-used assessment, the , developed in 2013 at the University of Pennsylvania. It consists of a series of cognitive tasks that measure subjects’ accuracy and speed in several major cognitive domains, including working memory, abstraction, sustained attention, episodic memory and processing speed. 

MindPrint has administered the assessment periodically to its clients over the past decade. The most recent rounds totaled 35,000 students and 4,000 teachers in 27 states.

By most measures, U.S. students are suffering. Last year, NAEP scores showed the average 13-year-old’s understanding of math dropping to levels last seen in the 1990s and reading levels dropping to 1971, when the test was first administered.

More recent research has shown that while older children are showing encouraging signs of academic recovery, younger kids aren’t making the same progress. Many students who weren’t even in a formal school setting when COVID hit are already falling behind — especially in math.

The Penn assessment found that children who attended elementary or pre-school during the pandemic and who are now 8 to13 years old showed the largest declines in memory. 

“Younger kids haven’t really developed a lot of these core cognitive skills,” Tsai said. “It hasn’t solidified for them, either through development or just through practice in the classroom. And so younger kids are more vulnerable to these pandemic shifts.”

But students across all age groups showed worse flexible thinking, which researchers now theorize contributes to lower academic performance — as well as challenging behaviors.

Tsai said kids from lower income backgrounds were more vulnerable to these changes, specifically in verbal reasoning and verbal memory, than their higher income peers, with bigger declines in verbal scores, which are highly correlated with academic achievement in all subjects.

Adults in the study had similar declines in both memory and flexible thinking, possibly explaining higher reported levels of and .

Nancy Weinstein, MindPrint’s CEO, said weaker flexible thinking isn’t necessarily a problem for experienced teachers who have developed strategies to cope with stressful situations and can modify plans on the fly. But those with less experience may be unable to change gears when lessons go astray or students act out in class. That may lead to higher teacher burnout.

Across the board, teachers’ skills suffered in areas such as verbal and abstract reasoning, spatial perception, attention and working memory, but they saw the greatest losses in verbal memory and flexible thinking.

“If we care about that, we need to know how to help them,” Weinstein said. “And there are some tried and true things you can do.”

She said schools should consider sharing data like this with teachers so they can understand that their frustration in class might not be due to students alone. That could make a big difference, she said, in “their willingness to put in the effort to change, as opposed to saying, ‘Why bother?’”

For students, Weinstein said, offering them more opportunities to practice skills with between study sessions could help. Schools should also consider “” techniques that break learning into chunks and address each individually.

Could such techniques help students — and teachers — regain a measure of pre-pandemic skills? Weinstein suggests the answer is “Yes.”

“The environment will matter, but certainly we can regain some of that if we do the right things,” she said. “And we know what the right things are to do.”

Crystal Green-Braswell, coordinator of staff wellness and culture for the Little Rock School District in Arkansas, said offering the Penn assessment to teachers and staff has helped many think more deeply about their work — and about their own thinking. 

“People who have had the assessment will say, ‘Now, you know my processing speed is slower — y’all are going to have to give me a moment,’” she said. 

That’s a huge change in a profession in which most workers have been asked “to take ourselves out of the equation and just get the work done,” Green-Braswell said. 

She sees offering such insights to educators as part of “rehumanizing” teaching. “When we provide this kind of assessment and we provide this kind of space for folks to actually get to know themselves, we are humanizing this profession and helping people to realize, ‘You play a role. You play an active role. You matter.’ ”

As the founder of in 2015, he enlisted university education school deans to incorporate findings from into teacher preparation. Before that, he spent five years as policy director of the , which underwrites new models of schooling. In his new endeavor, , which he calls “a think-and-do tank,” he’s pushing to help people think not only about how we learn, but how generative artificial intelligence (AI) works — and why they’re different.

His and regularly poke holes in high-flying claims about the power of AI-powered tutors — he recently offered choice words for Khan Academy founder Sal Khan’s of Open AI’s new GPT4o tool, saying it was “deployed in the most favorable educational environment we can possibly imagine,” leaving open the possibility that it might not perform so well in the real world.

In April, Riley ruffled feathers in the startup world in the journal Education Next that took and other AI-related companies to task for essentially using students as guinea pigs.

In the essay, he recounted asking to help him simplify an algebraic equation. Riley-as-student got close to solving it, but the AI actually questioned him about his steps, eventually asking him to rethink even basic math, such as the fact that 2 + 2.5 = 4.5.

Such an exchange isn’t just unhelpful to students, he wrote, it’s “counterproductive to learning,” with the potential to send students down an error-filled path of miscalculation, misunderstanding and wasted effort.

The interview has been edited for length and clarity.

The 74: We’re often so excited about the possibilities of ed tech in education that we just totally forget what science says about how we learn. I wonder if you have any thoughts on that.

Benjamin Riley: I have many. Part of my frustration is that we are seemingly living in a moment where we’re simultaneously recognizing in other dimensions where technology can be harmful, or at least not beneficial, to learning, while at the same time expressing unbridled enthusiasm for a new technology and believing that it finally will be the cure-all, the silver bullet that finally delivers on the vision of radically transforming our education system. And yeah, it’s frustrating. Ten years ago, for example, when everybody was excited about personalization, there were folks, myself included, raising their hand and saying, “Nope, this doesn’t align with what we know about how we think and learn. It also doesn’t align with the science of how we collectively learn, and the role of education institutions as a method of culturally transmitting knowledge.” All of those personalized learning dreams were dying out. And many of the prominent, incredibly well-funded personalized learning efforts either went completely belly-up, like , or have withered on the vine, like some of the public schools now named .

Now AI has revived all of those dreams again. And it’s frustrating, because even if it were true that personalization were the solution, no one 10 years ago, five years ago, was saying, “But what we need are intelligent chatbot tutors to make it real.” So what you’re seeing is sort of a commitment to a vision. Whatever technology comes along, we’re going to shove into that vision and say that this is going to deliver it. I think for the same reasons it failed before, it will fail again. 

You’re a big fan of the University of Virginia cognitive scientist , who has done a lot to popularize the science of how we learn.

He’s wonderful at creating pithy phrases that get to the heart of the matter. One of the counterintuitive phrases he has that is really powerful and important is that our minds in some sense “are not built to think,” which feels really wrong and weird, because isn’t that what minds do? It’s all they do, right? But what he means is that the process of effortful thinking is taxing in the same way that working out at the gym is taxing. One of the major challenges of education is: How do you wrap around that with students, who, like all of us, are going to try to essentially avoid doing effortful thinking for sustained periods? Over and over again, technologists just assume away that problem.

In the case of something like large language models, or LLMs, how do they approach this problem of effortful thinking? Do they just ignore it altogether?

It’s an interesting question. I’m almost not sure how to answer it, because there is no thinking happening on the part of an LLM. A large language model takes the prompts and the text that you give it and tries to come up with something that is responsive and useful in relation to that text. And what’s interesting is that certain people — I’m thinking of most prominently — have talked about how amazing this is conceptually from an education perspective, because with LLMs you will have this infinitely patient teacher. But that’s actually not what you want from a teacher. You want, in some sense, an impatient teacher who’s going to push your thinking, who’s going to try to understand what you’re bringing to any task or educational experience, lift up the strengths that you have, and then work on building your knowledge in areas where you don’t yet have it. I don’t think LLMs are capable of doing any of that.

As you say, there’s no real thinking going on. It’s just a prediction machine. There’s an interaction, I guess, but it’s an illusion. Is that the word you would use?

Yes. It’s the illusion of a conversation. 

In your Education Next essay, you quote the cognitive scientist , who says LLMs are “frequently wrong, but never in doubt.” It feels to me like that is extremely dangerous in something young people interact with.

Yes! Absolutely. This is where it’s really important to distinguish between the now and the real and the present versus the hypothetical imagined future. There’s just no question that right now, this “hallucination problem” is endemic. And because LLMs are not thinking, they generate text that is factually inaccurate all the time. Even some of the people who are trying to push it out into the world acknowledge this, but then they’ll just put this little asterisk: “And that’s why an educator must always double-check.” Well, who has the time? I mean, what utility is this? And then people will say, “Well yes, but surely it’s going to get better in the future.” To which I say, Maybe, let’s wait and see. Maybe we should wait until we’ve arrived at that point before we push this out.

Do we know how often LLMs are making mistakes?

I can say just from my own personal usage of Khanmigo that it happens a lot, for reasons that are frankly predictable once you understand how the technology works. How often is it happening with seventh-grade students who are just learning this idea for the first time? We just don’t know. [In response to a query about errors, Khan Academy sent links to two on its site, noted that Khanmigo “occasionally makes mistakes, which we expected.” It also pointed, among other things, that Khanmigo now uses a calculator to solve numerical problems instead of using AI’s predictive capabilities.]

One of the things you say in the EdNext piece is that you just “sound like a Luddite” as opposed to actually being one. The Luddites saw the danger in automation and were trying to push against it. Is it the same, in a way, as what you’re doing? 

Thank you for asking that question because I feel my naturally contrarian ways risk painting me into a corner I’m really not in. Because in some sense, generative AI and large language models are incredible — they really are. It is a remarkable achievement that they are able to produce fluent and coherent narratives in response to just about any combination of words that you might choose to throw at them. So I am not a Luddite who thinks that we need to burn this all down.

There are methods and ways, both within education and in society more broadly, in which this tool could be incredibly useful for certain purposes. Already, it’s proving incredibly stimulating in thinking about and understanding how humans think and learn, and how that is similar and different from what they do. If we could just avoid the ridiculous overhype and magical thinking that seems to accompany the introduction of any new technology and calm down and investigate before pushing it out into our education institutions, then I think we’d be a lot better off. There really is a middle ground here. That’s where I’m trying to situate myself. 

Maybe this is a third rail that we shouldn’t be touching, but I was reading about Thomas Edison and his ideas on education. He had a great quote about movies, which he thought would revolutionize classrooms. He said, “The motion picture will endure as long as poor people exist.” It made me think: One of the underlying themes of ed tech is this idea of bringing technology to the people. Do you see a latent class divide here? Rich kids will get an actual personal tutor, but everybody else will get an LLM? 

My worry runs differently than that. Again, back to the Willingham quote: “Our minds are not built to think.” Here’s the harsh reality that could indeed be a third rail, but it needs to be acknowledged if we’re going to make meaningful progress: If we fail in building knowledge in our students, thinking gets harder and harder, which is why school gets harder and harder, and why over time you start to see students who find school really miserable. Some of them drop out. Some of them stop trying very hard. These folks — the data is overwhelming on this — typically end up having lives that are shorter, with less economic means, more dire health outcomes. All of this is both correlated and interrelated causation.

But here’s the thing: For those students in particular, a device that alleviates the cognitive burden of schooling will be appealing. I’m really worried that this now-widely available technology will be something they turn to, particularly around the incredibly cognitively challenging task of writing — and that they will continue to look to this as a way of automating their own cognition. No one really needs to worry about the children of privilege. They are the success stories academically and, quite frankly, many of them enjoy learning and thinking and will avoid wanting to use this as a way of outsourcing their own thinking. But it could just make the existing divide a lot wider than it is today — much wider.

How is education research responding to AI?

The real challenge is that the pace of technology, particularly the pace of technological developments in the generative AI world, is so fast that traditional research methods are not going to be able to keep up. It’s not that there won’t be studies — I’m sure there are already some underway, and there’s tiny, emerging studies that I have seen here and there. But we just don’t have the capabilities as a research enterprise to be doing things the traditional way. A really important question that needs to be grappled with, as a matter of policy, potentially as a matter of philanthropy and just as a matter of society, is: So, what then? Do we just do it and hope for the best? Because that may be what ends up happening.

As we’ve seen with and in schools, there can be real impacts that you don’t realize until five, 10 years down the road. Then you go back and say, “Well, I wish we’d been thinking about that in advance rather than just rolling the dice and seeing where it came up.” We don’t do that in other realms of life. We don’t let people just come up with medicines that they think will cure certain diseases and then just say, “Well, we’ll see. We’ll introduce it into broader society and let’s figure it out.” I’m not necessarily saying that we need the equivalent per se, but something that would give us better insight and real-time information to help us figure out the overall positives and not-so-positives seems to me a real challenge that is underappreciated at the moment.