Business & Entrepreneurship

How online gambling ads may draw bettors back

Megan M Rogers — Fri, 16 Oct 2026 16:56:29 +0000

How online gambling ads may draw bettors back Megan M Rogers Fri, 10/16/2026 - 10:56

New research from the College of Communication, Media, Design and Information examines how online gambling services target bettors with digital advertising—and how that exposure is associated with subsequent gambling activity.

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Fear of political doxing may push some Americans out of public life

Katy Hill — Tue, 13 Oct 2026 15:40:19 +0000

Fear of political doxing may push some Americans out of public life Katy Hill Tue, 10/13/2026 - 09:40

Katy Marquardt Hill

Running for office, speaking up at public meetings or sharing political views online can come with personal risks. A new study suggests that politically motivated doxing, the publication of personal information intended to intimidate or harass someone because of their political views, may be discouraging some Americans from participating in public life.

Jason Thatcher

Researchers found that people exposed to a simulated doxing attack were less willing to run for office, engage in public debate or take on community leadership roles. The effect was especially pronounced among people whose views were less firmly held.

“Our findings suggest that when people get doxed, those in the middle are often the first to stop talking,” said Jason Thatcher, professor of information systems at the Leeds School of Business and co-author of the study. “People in the middle tend to disengage because they don’t want to be attacked.”

The research notes that politically motivated doxing is increasingly being used to intimidate opponents and discourage civic engagement. A 2024 survey by the Anti-Defamation League found that 22% of Americans experienced severe online harassment in the previous year.

The fear factor

The study, published in March 2026 in MIS Quarterly, looked at how politically motivated doxing can affect civic participation.

Thatcher said politically motivated doxing often starts online but can have consequences beyond the internet. According to the study, participants associated it with threats to their safety, employment, reputation and ability to participate in public life.

The researchers, who also included Annika Stäcker and Julia Hett of the University of Münster in Germany, designed an online experiment involving 300 U.S. participants. They chose to focus on local school board elections, where political disagreements have increasingly spilled into personal attacks and harassment.

Participants were asked to imagine being nominated to serve on their local school board. Researchers showed them a personalized doxing scenario built around their actual views on abortion. The simulated attack included personal details participants had voluntarily shared and was designed to closely resemble real-world doxing campaigns. Participants then described how the experience made them feel and answered questions about whether they would continue engaging in public life, including running for office or discussing political issues online.

Thatcher said making the survey more personal helped the researchers uncover reactions that might not have appeared in a more abstract format.

“What's unique about this study is the authenticity,” Thatcher said. The researchers tailored each scenario using details from participants' lives, including their hometown and political views, so the experience felt personal and immediate.

Participants tended to react to politically motivated doxing in one of two ways: fear or anger. Fear was the more common response, particularly among participants who viewed the incident as severe. Those who felt afraid were less likely to say they would continue running for school board or engaging in political discussions online. Anger, on the other hand, sometimes motivated people to stay involved in civic life.

The most surprising finding was who was most likely to disengage.

The researchers expected participants with the strongest convictions to react most intensely. Instead, people with less firmly held views experienced stronger emotional reactions and were more likely to say they would withdraw from civic engagement after the doxing scenario.

The result was what the researchers describe as a selective silencing effect. The findings suggest that if people with less firmly held views are more likely to disengage, public debate may increasingly be shaped by those with the strongest convictions.

Thatcher said the finding points to a broader concern about who participates in public life.

“This is so anti-American, because what we've been known for for 250 years is we have a right, we have a left and we have a middle, and the middle has driven the discourse in this country,” he said. “When people drive the middle out of the conversation, that's a problem for society.”

Why people pull back

Participants described the simulated doxing experience as a loss of control over their personal information, a forced exposure to hostile audiences and a threat that could not easily be undone. Many worried about their own safety or the safety of family members. Others described feeling isolated from their communities, as if their views were no longer welcome or protected.

One study participant said the incident made them feel as if “there is no hope for society when other people cannot respect others' views and when voicing your opinion, you feel unsafe doing so.”

The findings offer some ideas for how policymakers and online platforms might respond. The researchers suggest that stronger protections, clearer ways to address doxing incidents and resources such as counseling, legal support and online safety education could help people stay involved in public life. They also suggest that platforms do more to catch and remove doxing content before it spreads.

The researchers also noted that the study captured people’s immediate reactions to a simulated doxing incident, not how they might behave over time. They said future research should also look at other political and social settings where doxing occurs.

Thatcher said the larger lesson extends far beyond any single target.

“As scary as the individual harm is, the scariest part of this whole study, to me, is that implication for participation in society,” he said.

As the midterm elections approach, new research suggests politically motivated doxing can discourage civic participation, particularly among people with less firmly held political views.

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CUriosity: Why are we so obsessed with pumpkin spice?

Katy Hill — Thu, 08 Oct 2026 12:39:50 +0000

CUriosity: Why are we so obsessed with pumpkin spice? Katy Hill Thu, 10/08/2026 - 06:39

Katy Marquardt Hill

In CUriosity, experts across the ��ñ�� campus answer questions about humans, our planet and the universe beyond.

Heather Kennedy, who teaches marketing at the Leeds School of Business, explains the cultural phenomenon that is pumpkin spice and why Americans can't seem to quit it.

Credit: Adobe Stock

Pumpkin spice season doesn't arrive anymore. It invades.

Every fall, pumpkin spice claims a little more territory. Grocery store shelves overflow with pumpkin spice creamers, cookies, spreads and cereals. These days, that relentlessly seasonal blend of cinnamon, ginger, nutmeg and allspice has moved well beyond food, turning up in everything from body wash and face masks to garbage bags and multipurpose cleaner. The craze has even reached the afterlife, in the form of a pumpkin spice casket.

How did such a humble baking spice become an annual cultural event? According to Heather Kennedy, an associate teaching professor of marketing at ��ñ��, the answer has as much to do with psychology as flavor.

“People love fall. They love the sense of it. They love the ritual around it,” Kennedy said.

That's part of what gives pumpkin spice its staying power. The flavor has become shorthand for autumn, bringing to mind everything from changing leaves to cozy sweaters to Thanksgiving pie.

“When we associate a flavor to that, it gives us a sense of nostalgia,” Kennedy said.

Starbucks didn't invent pumpkin spice

Pumpkin spice may feel like a modern invention, but the flavor has been around almost as long as the United States.

References to pumpkin spice appeared in cookbooks in the late 1700s, Kennedy said. By the 1930s, McCormick was selling pumpkin pie spice blends in stores.

When Starbucks introduced the Pumpkin Spice Latte in 2003, it moved a flavor people knew from their kitchens into something they could sip on their morning commute.

“Starbucks really created something that already existed culturally,” Kennedy said. “They commercialized something that already existed in the minds of consumers.”

Why pumpkin spice keeps winning

Pumpkin spice benefits from an established marketing principle: making people wait for what they want.

“That limited amount of time creates scarcity and creates a certain excitement around it,” Kennedy said.

After all, there's nothing stopping Starbucks from selling Pumpkin Spice Lattes year-round. But the drink vanishes each winter and reappears every fall, turning its arrival into an annual event.

“It becomes ritualistic that every fall, I'm going to have a pumpkin spice latte to kick off the season,” Kennedy said.

But pumpkin spice didn't go viral just because Starbucks kept talking about it; it went viral because everyone else did.

“Starbucks let go of control of the messaging and not only allowed but encouraged people to create social media content around the excitement of pumpkin spice,” Kennedy said.

Previously in CUriosity

CUriosity: How special are the Boulder Flatirons, really?

Read more CUriosity stories

As consumers posted photos of their first PSL of the season and debated whether pumpkin spice was arriving before fall itself, the drink became something bigger than a seasonal promotion. Love it or hate it, people still can't stop talking about it.

Too basic to fail

Pumpkin spice may be wildly popular, but it also has plenty of critics. After years of pumpkin spice products spreading from coffee shops into nearly every corner of the grocery store, some consumers have developed a case of pumpkin spice fatigue.

“People get tired of it,” Kennedy said.

As coffee shops roll out banana bread lattes, pecan cortados, caramel apple cold brews and other fall alternatives, some consumers seem eager to move on from pumpkin spice.

The flavor has even acquired an unflattering label: basic.

But Kennedy suspects the backlash may be part of the secret.

“People love to talk about it and post about it,” she said.

Every fall brings a new round of eye-rolls, jokes and declarations that pumpkin spice has finally gone too far. But all that attention keeps the flavor in the spotlight.

Kennedy has another theory about at least some of the critics.

“People think it's basic, and they don't want to be associated with it,” she said. “But I'm going to guess that some of those people are secretly buying a pumpkin spice latte.”

Heather Kennedy, who teaches marketing at the Leeds School of Business, explains the cultural phenomenon that is pumpkin spice and why Americans can't seem to quit it.

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Think a human reviewer can catch an AI mistake? Facial-recognition study suggests it's complicated

Katy Hill — Wed, 16 Sep 2026 15:45:00 +0000

Think a human reviewer can catch an AI mistake? Facial-recognition study suggests it's complicated Katy Hill Wed, 09/16/2026 - 09:45

Katy Marquardt Hill

Facial-recognition systems often rely on a relatively simple safeguard. AI identifies a potential match, then a human reviews it before any action is taken.

But new research suggests the effectiveness of that safeguard may depend on who the human reviewer is.

The study, published in February 2026 in the Journal of Applied Research in Memory and Cognition, found that humans and facial-recognition algorithms generally agree most with people who scored highest on tests of face-recognition ability. But people vary widely in their ability to tell faces apart, meaning some human reviewers may be much better than others in evaluating an AI-generated match.

"We found that the AI systems are actually pretty good overall," said David Dobolyi, lead author of the study and assistant professor of organizational leadership and information analytics at the Leeds School of Business. "They are able to perform similarly to higher-scoring individuals."

David Dobolyi

Those high-scoring people are sometimes called "super recognizers." People vary widely in how well they can tell faces apart, and the study found that stronger face recognizers were much more likely to see facial similarities the same way the algorithms did. Researchers measured those differences using a standard face-recognition test administered to nearly 4,000 participants.

That finding matters because many facial-recognition systems operate with a "human in the loop." The software generates potential matches, but a person reviews the results before any action is taken.

"I wouldn't say that having a human in the system solves the problem," Dobolyi said. "If you take the average human, that person may not be better than a specific model."

"I think just like an AI model, you need to have a measure of how good a person is at this task," he said.

That complicates a common narrative around facial recognition that AI makes the mistakes and humans catch them.

Dobolyi said the research reveals a more complicated reality: Both humans and algorithms have strengths, weaknesses and blind spots.

"People have their own biases. These systems have their own biases," he said. "It's not about one being better than the other. It's about understanding the pros and cons of each, or what one system is good at, what one system is bad at, including among people."

Different algorithms, different results

The researchers also found that not all facial-recognition systems see faces the same way.

When researchers compared six commercial and open-source systems, they found differences in how closely the algorithms aligned with human judgments. They also found that agreement sometimes differed depending on whether participants were comparing Black or white faces. But those differences varied from one facial-recognition system to another.

Could you be a super-recognizer?

Think you’re unusually good at recognizing faces? Take the free, research-based UNSW Face Test, developed by researchers at the University of New South Wales. The test measures face-recognition ability and shows how your performance compares with other participants.

Note: The test is a screening tool not a clinical diagnosis. Researchers generally use multiple tests to identify super-recognizers.

For Dobolyi, that's a reminder that organizations shouldn't assume all facial-recognition tools work equally well.

"This is why it's important to do this kind of work, because you need to ensure that your system is fair across the board," he said. "You don't want to have any spot where you can't trust the outputs of the AI system."

Another challenge is that many commercial AI systems don't show their work.

"We don't really have explanations for why an AI system makes the decision it does," Dobolyi said. "So there's still this black box problem."

The study builds on another line of research Dobolyi has pursued on the role AI can play in high-stakes decisions. In a 2025 paper published in Proceedings of the National Academy of Sciences, he and his co-authors found that AI tools could help reduce bias in how people evaluate eyewitness testimony.

Both studies arrive at a similar conclusion. AI has the potential to improve decision-making, but it works best when people understand what it can do, what it can't and when to trust its recommendations.

As facial-recognition technology becomes more common, Dobolyi said he hopes organizations become more transparent about what these systems can and can't do and more rigorous about evaluating them before putting them to use.

"I think the study emphasizes the importance of more testing and more openness about what the capabilities of systems like this really are, what their limitations are, and the level of confidence and trust we should place in them," he said.

A new study out of the Leeds School of Business suggests human oversight isn't always the safeguard people assume it is.

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What employers get wrong about medical leave

Katy Hill — Wed, 02 Sep 2026 16:45:41 +0000

What employers get wrong about medical leave Katy Hill Wed, 09/02/2026 - 10:45

Katy Marquardt Hill

When a serious health crisis strikes, medical leave can give employees the time and space they need to recover. But new research finds that a leave policy alone isn’t enough. The way companies put those policies into practice can either support employees through a difficult time or make a challenging experience even harder.

“These policies aren’t just administrative policies, nice-to-haves—they can literally save employees’ lives,” said Christina Lacerenza, assistant professor of organizational leadership and information analytics at the Leeds School of Business and co-author of the study. “When we just can’t keep living and working at the same pace, one of the few available resources to us is a medical leave of absence.”

The study, published in the Journal of Applied Psychology in April 2026, examined what medical leave is actually like for the people going through it. Between 2022 and 2025, the researchers, Liza Barnes of Drexel University's LeBow College of Business and Ashley Hardin of Olin Business School at Washington University in St. Louis, interviewed 48 participants, including 30 employees who had taken medical leave and 18 leave coordinators who helped guide employees and organizations through the process.

Christina Lacerenza

Most medical leaves in the study lasted between four and 26 weeks and involved a range of physical and mental health conditions. The conversations examined experiences before, during and after leave from both the employee and coordinator perspectives. Researchers then analyzed all 48 interviews for recurring patterns and themes.

More than a workplace disruption

Across dozens of conversations, a common theme emerged. Employees were experiencing profound suffering.

Many participants were already grappling with serious medical conditions, including cancer, mental health crises, chronic illnesses and traumatic injuries. The illness itself was often the source of immense physical and emotional strain. But the researchers found that employers could either ease that burden or make it worse through the way they handled leave requests.

Employees described four major forms of suffering during the leave process: loss of functioning, loss of safety, loss of stability and loss of autonomy. For many participants, the challenge wasn't just recovering from an illness. It was navigating uncertainty about their health, finances and careers all at once.

One interview in particular stayed with Lacerenza. A participant who worked in a newsroom recalled being told by her doctor that continuing to work could be fatal.

"I didn't want to leave the newsroom. My doctor essentially forced me out of the newsroom and said, 'If you don't leave, you will die,'" the employee told researchers.

Stories like that helped shape the researchers' thinking about medical leave. "We didn't intend to study suffering specifically," Lacerenza said. "But it emerged from the data and ultimately became the lens through which we came to understand the medical leave experience."

A healing-centered approach

The researchers found that the organizations that handled medical leave best treated it as part of the healing process, not just an HR requirement.

They identified four practices that helped employees recover: tailoring leave plans to individual circumstances, preparing for unexpected absences, reducing financial and job uncertainty and maintaining compassionate support throughout the process.

"The way medical leave is implemented is just as important as having the policy itself," Lacerenza said. "It's really critical to adopt this healing-centered approach so that employees have the time, support and space to truly focus on healing."

The study found that employees often had better experiences when a dedicated leave coordinator helped guide them through the process. Unlike managers, who may have little training on medical leave requirements, leave coordinators help employees navigate paperwork, understand their options and determine how much personal information they want to share.

Another important finding, Lacerenza said, is that fairness does not necessarily mean providing the same leave format for every employee.

"On the surface, consistency sounds like it would be fair," she said. But employees facing a cancer diagnosis, a serious accident or a mental health crisis may need very different forms of support. Someone undergoing cancer treatment, for example, might need intermittent leave for recurring appointments, while a worker recovering from surgery may need several consecutive weeks away from work.

"Fairness isn't about treating every employee identically," she said. "It's about providing each employee with the support they need to adequately heal and recover."

When good intentions aren't enough

Problems often arise not because organizations lack a medical leave policy, but because they haven't thought through how it will work in practice, Lacerenza said. Having a policy on the books doesn't necessarily mean employees will use it, benefit from it or feel supported while they're away, she added.

Another mistake companies make is creating a workplace culture where employees feel they can't step away, even when they need to. The researchers heard stories of workers who delayed taking leave because they worried about burdening colleagues or disrupting their team. In some cases, employees continued working despite worsening symptoms because they felt there was no clear plan for how their responsibilities would be handled in their absence.

"One of our participants talked about how her illness began via a mental health crisis, and she was just trying to push through work," Lacerenza said. "Ultimately what happened is she started physically getting sick."

Managers can unintentionally contribute to the problem, she added. Asking employees to postpone leave until after a busy season or questioning whether time off is really necessary can discourage workers from seeking help when they need it.

Lacerenza sees medical leave as part of a broader shift in how organizations support employees through illness, recovery and other major life challenges.

"I do think that the future is becoming more bright when it comes to handling these types of employee experiences," she said. "I think there's a bigger push to see and understand an employee as a whole person."

New Leeds School of Business research explores how workplace policies can support recovery or add to employee suffering.

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The rise of the bot boss

Katy Hill — Wed, 26 Aug 2026 14:54:41 +0000

The rise of the bot boss Katy Hill Wed, 08/26/2026 - 08:54

Katy Marquardt Hill

Imagine learning that the system deciding whether your work meets the mark isn't a manager—it's a bot.

As artificial intelligence becomes more common in the workplace, companies are increasingly using software to monitor and evaluate work. Whether it's AI measuring customer service performance or flagging unusual employee activity, workers are encountering more technologies that judge their work rather than help them do it.

"We often talk about AI as a tool, but increasingly we're seeing systems that provide oversight, quality assurance or monitoring functions,” said Jason Thatcher, professor of information systems at the Leeds School of Business and co-author of the study, published in January 2026 in MIS Quarterly.

The study found that when bots are given authority over people, workers push back.

"For many people, the idea of a bot evaluating their work feels uncomfortable,” he said. “You're a human expert performing a task, and suddenly a software system has the authority to say yes or no. That changes how people experience work."

Jason Thatcher

To understand how people respond when software gains that kind of authority, Thatcher and his co-authors, Nadine Kathrin Ostern and Marek Kowalkiewicz of Queensland University of Technology in Brisbane, Australia; Likoebe M. Maruping of the J. Mack Robinson College of Business at Georgia State University; and Jörg Weking of the Technical University of Munich, examined the 2010 rollout of ClueBot, Wikipedia's anti-vandalism bot.

Because Wikipedia relies on volunteers throughout the world to create and edit articles, ClueBot was designed to automatically remove harmful edits, such as spam, profanity or intentionally false information. At the time, it was catching about 70% of problematic edits identified as vandalism.

But unlike a spell-checker or other automated tool, ClueBot went beyond helping contributors do their work. It evaluated their edits and sometimes overruled them.

By analyzing weeks of public debates among editors, developers and Wikipedia leaders during the bot's approval process, the researchers examined what happened when human judgment challenged a machine's decisions. Contributors pushed back, causing developers to refine its rules, limit its authority and create formal ways for people to challenge its judgments.

What emerged, Thatcher said, was a process of humans figuring out how to work with machines. "Rather than bot regulation, it was actually a bot-human-bot negotiation," he said. In other words, contributors couldn’t change the bot themselves, but they could appeal to the people who could.

"What was cool about this study, that got me so excited, was people pushed back," Thatcher said. "While they couldn't get the bot to change, they could go to that committee that made the decisions about what the bot could or could not do, who would then go back to the developers, who would tweak the bot."

Human oversight

The lesson goes beyond Wikipedia, Thatcher said. As organizations increasingly use AI to monitor and review work or enforce standards, they’ll also need to think about what happens when those systems get things wrong and who gets a say in correcting them.

"Human voice still matters," Thatcher said. "The content creator, or the Wikipedia contributor, still has a voice in how the bots operate when the bots don't operate properly."

That's what happened on Wikipedia. Over time, those efforts led to lower error rates, better ways to review the bot's work and formal ways for humans to influence its decisions.

The future workplace may include more bots evaluating human work, Thatcher said, but the most successful organizations won't be the ones that remove people from the process. They'll be the ones that leave room for employees to challenge, correct and improve the technology.

"We have to keep humans in the loop," Thatcher said. "Users and experts need to stay engaged so we're building systems that solve problems rather than create them."

What happens when AI starts evaluating workers? New ��ñ�� research out of the Leeds School of Business offers some answers.

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The biggest AI mistake students are making right now

Katy Hill — Tue, 18 Aug 2026 16:21:29 +0000

The biggest AI mistake students are making right now Katy Hill Tue, 08/18/2026 - 10:21

Katy Marquardt Hill

As artificial intelligence becomes more common in the workplace, students face a new challenge: learning how to use AI without letting it do the thinking for them. Employers increasingly expect graduates to know their way around AI, but they also want problem-solvers who can adapt as the technology evolves.

Jeremiah Contreras

"If students learn how to use AI to challenge their reasoning and sharpen their judgment instead of replacing it, they'll be miles ahead,” said Jeremiah Contreras, associate teaching professor of accounting at the Leeds School of Business and AI-in-education expert. ��ñ�� Today recently spoke with Contreras about the biggest AI mistake students are making, what employers want to see from new graduates and how students can use AI to become better thinkers, not just faster workers.

What’s changed about how students should think about AI this school year?

More businesses have started identifying ways AI can help them operate more effectively. At the same time, they're looking for students who can help find those solutions.

There's now an expectation that students bring curiosity, a basic understanding of AI tools and some level of competency using them.

The biggest change is that those expectations are stronger than they were even a year or two ago. AI has shifted from being a technology with potential to one that's increasingly proving useful in the workplace.

Are employers expecting more from entry-level hires now?

The specific AI tools employers use will keep changing. What they're really looking for is someone who can step back, understand a problem and evaluate which tools or approaches make the most sense for solving it. The solution that works today may not be the solution six months from now.

I'd rather have a student identify a business problem and develop a solution that isn't perfect than provide the perfect answer to a problem someone else, or AI, identified. The ability to recognize opportunities, define the right problem, and use technology to develop and refine solutions is often more valuable than finding the perfect answer on the first attempt. The problem-solving process is often more important than the final solution.

The key is learning how to think about why a tool solves a problem, not just how to use a specific tool.

What helps students stand out in job interviews?

Employers are looking for proof. They want to see that you've actually done something, not just that you've learned about it.

If a student can say, "I had this problem and built a solution using AI," that tends to be much more compelling than simply saying they took a class.

More employers are asking candidates to solve problems using AI during interviews. They are interested in how candidates approach a problem, use AI appropriately, and explain their thinking. The process often matters more than the final answer.

What mistakes are students making with AI right now?

Some students take the approach that if they put an assignment into AI, they'll get an answer and earn a good grade.

The biggest mistake is relying too much on AI to do the work.

AI can be great at explaining a concept in a different way or helping you better understand something. But you still need to go back, organize your thoughts and make sure you can explain the material yourself.

What's the right role for AI in the learning process?

Students should use AI to support learning, especially in areas where they already have some familiarity and want another perspective or explanation. When learning something new, textbooks, course materials and other authoritative sources are still important. AI is a great tutor, but it should not replace foundational learning. As students gain expertise, they'll also get better at recognizing when AI gets something wrong.

What does it actually mean to be prepared for an AI-driven world?

From an educational standpoint, we still have to make sure students learn the fundamentals in a world where AI can do many assignments for them. There's a blurry line between using AI to learn and letting it take on too much of the cognitive load.

That's where what I call "productive friction" comes in. AI makes things very efficient. From a business perspective, that's great. But from a learning perspective, students still need to struggle with concepts to truly understand them.

Ultimately, businesses want students who are comfortable with AI. But even more important is the ability to evaluate problems, identify solutions and adapt as technology changes.

How can students use AI in a way that actually makes them smarter, not just faster?

A common approach is to ask AI for an answer, read it and think, "That makes sense."

A better approach is to ask AI to challenge your thinking. Have it ask questions. Have it critique your reasoning. Have it push you to explain your ideas more clearly.

The goal is to use AI as part of the learning process rather than as a shortcut around it.

Can you give an example of using AI well versus using it poorly?

One approach is to upload an assignment, have AI do most of the work and submit the output with very little effort.

A better approach is for a student to think through the assignment first, develop some ideas and then use AI to challenge or refine that thinking.

What are some practical ways students can build AI skills right now?

Start by identifying a problem in your own life and trying to solve it with technology. Maybe it's comparing colleges. Maybe it's planning meals. Maybe it's managing a schedule or budget.

If students learn how to identify a problem, experiment with tools and build a solution, that translates directly into the kinds of skills employers value.

The more students explore, experiment and stay curious, the better prepared they'll be.

What's an example of a project that would impress an employer?

I had a student build an app to help track personal expenses. Years ago, that would have required a skilled coder. Today, students can create simple solutions much more easily.

It wasn't a business-ready product, but it solved a real problem. Employers love seeing students identify a problem and solve it in an innovative way.

What should students do when schools restrict AI but workplaces expect it?

I think about AI the same way I think about a tutor. I wouldn't want a tutor to do my work for me. I'd want a tutor to explain concepts, challenge my thinking and help me learn.

If a class doesn't allow AI on an assignment, there may be a learning reason behind that policy. Students should respect it.

At the same time, when course policies allow it, they can often use AI outside of the assignment itself to better understand a concept and prepare to do the work on their own.

Do students need to learn specific AI tools?

Not necessarily. It's more important to learn how to think through problems and find the right tool for a given situation.

Curiosity is key. The tools will change. The ability to evaluate a problem, research solutions and adapt is still fundamentally human.

Looking ahead, what will separate students who thrive from those who struggle?

The students who thrive will be the ones who stay curious.

There's no single right way to use AI because people learn differently. The key is figuring out how AI can help you learn more deeply rather than simply work faster.

Students who learn how to do that will be more effective, more adaptable and better prepared for whatever comes next.

��ñ�� Today regularly publishes Q&As on news topics through the lens of scholarly expertise and research/creative work. The responses here reflect the knowledge and interpretations of the expert and should not be considered the university position on the issue. All publication content is subject to edits for clarity, brevity and university style guidelines.

Leeds professor and AI-in-education expert Jeremiah Contreras says students who use AI to challenge their thinking, not replace it, will have an edge in school and the workplace.

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Remote workers report the highest well-being in study of 7,700 employees

Katy Hill — Wed, 12 Aug 2026 17:19:19 +0000

Remote workers report the highest well-being in study of 7,700 employees Katy Hill Wed, 08/12/2026 - 11:19

Katy Marquardt Hill

For years, many employers have worried that work-from-home arrangements leave employees isolated, disconnected from coworkers and more likely to leave their jobs.

But according to a new study, remote workers are doing better than many employers realize.

Researchers analyzed survey data from 7,704 employees at a large healthcare organization. One pattern stood out: Employees who worked fully remotely reported the highest levels of well-being, while those who worked entirely onsite reported the lowest. The study also found little evidence that remote workers felt less connected to colleagues or workplace culture.

"This suggests you let people work remotely if they want to work remotely," said Stefanie Johnson, professor of organizational leadership and information analytics at the Leeds School of Business and co-author of the study, published in July 2026 in the journal Frontiers in Psychology. "Taking away people's choice of how they work is probably not going to help them in terms of their well-being."

Stefanie Johnson

As companies continue to debate remote work, many leaders worry that employees need to be in the office to stay connected, work well together and remain committed to their organization. Johnson said the research doesn't always support those concerns.

"The data from our study and others suggest remote and hybrid work result in better outcomes than return-to-office mandates," she said. "Leaders are not making decisions based on data. I think they are just returning to what they are used to."

Rethinking remote work

Johnson, who co-authored the study with Alyssa Lezcano, Stephanie Zajac and Courtney Holladay of the MD Anderson Leadership Institute in Houston, said the results surprised her. She thought employees who split their time between home and the office might have the best of both worlds.

"I actually thought you would be happiest if you were part time out of the office," she said. "Then every once in a while you get to see people, get that human connection."

Instead, the data pointed in a different direction.

Among employees in the study, well-being was highest for fully remote workers, followed by hybrid employees and then onsite workers.

The findings also cast doubt on one of the main arguments for bringing employees back to the office: that people need to be together in person to feel connected.

Employees in the study were asked to describe their organization's culture in a handful of words. Remote workers were slightly more likely than their hybrid and onsite peers to use words associated with teamwork, inclusion and support.

"People who are remote actually said more things that indicated they had more positive connections, even though they were remote," Johnson said.

Still, Johnson said face-to-face interaction can play an important role in helping coworkers build relationships, especially if they are just starting out in their careers.

"Remote works better after you know people," she said. "So there is still a benefit of having some face time."

Staying power

The researchers also examined employee turnover one year after the survey was completed.

They found that employees with higher well-being were less likely to leave the organization. Work location itself was not a strong direct predictor of turnover. Instead, remote work was associated with higher well-being, which in turn was associated with lower turnover.

"It makes sense. If you have higher well-being, you're less likely to leave your job," Johnson said.

Participants completed the workplace survey in 2023, and researchers compared those responses with actual turnover records one year later. Of the employees surveyed, roughly half worked onsite, with the remainder split between hybrid and fully remote arrangements.

Flexibility matters

The study did not explore why remote workers reported higher well-being, but Johnson points to a growing body of research on autonomy and flexibility.

One explanation is that remote workers have greater control over their work setup and daily schedule, she said.

"If you have control over your environment, you tend to have more positive outcomes," she said.

Working from home can also eliminate many everyday stressors.

"Spending a lot of time in traffic is negatively related to well-being," Johnson said. "There are so many little stressors associated with being in the office."

Those stressors can include arranging child care, hiring help for pets or managing the logistics of getting to and from work, she said.

Johnson said the study points to a broader lesson for employers navigating return-to-office debates. Rather than focusing only on where employees work, organizations may get better results by investing in employee well-being.

"I think flexibility is here to stay," she said.

Many employers worry remote workers feel disconnected. A new study found they reported higher well-being and were no more likely to feel isolated than their onsite peers.

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Megan M Rogers — Thu, 06 Aug 2026 16:00:26 +0000

The silver tsunami: What's next for America's small businesses? Megan M Rogers Thu, 08/06/2026 - 10:00

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The world's largest study of human behavior is happening online

Katy Hill — Wed, 05 Aug 2026 14:40:45 +0000

The world's largest study of human behavior is happening online Katy Hill Wed, 08/05/2026 - 08:40

Katy Marquardt Hill

Every day, billions of people document their lives online. They post on social media, write product reviews, leave comments about their workplaces, share health concerns in patient portals and discuss everything from politics to parenting in online forums. Collectively, the conversations offer a glimpse into how people navigate everyday life.

Leeds School of Business Professor of Information Systems Kai Larsen believes researchers are only beginning to understand how useful those insights could be.

Kai Larsen

For generations, surveys have been one of the primary tools researchers use to study human behavior. But people today generate far more social media posts, reviews, comments and other forms of text than survey responses.

"We're estimating that it's about a million times more of these texts available for analysis than there are surveys we can ever do," Larsen said. "So we're drastically opening up the space for doing psychological research."

To help researchers make sense of that vast body of language, Larsen developed PsyProxy, a new class of AI models designed to translate written language into measurable psychological concepts. Rather than simply identifying whether a piece of text is positive or negative, the tool measures psychological concepts embedded in language.

Donald Edmondson, a behavioral medicine researcher at Columbia University who collaborates with Larsen, said PsyProxy's potential lies in its ability to turn people's own words into measurable psychological variables.

"People are constantly expressing psychologically meaningful information in their own words," Edmondson said. "Those data are rich in detail, and they often contain information researchers would never have thought to ask about directly."

Traditionally, understanding concepts such as trust, stress, loneliness, burnout or life satisfaction required designing surveys, recruiting participants and convincing people to participate.

Larsen believes PsyProxy can help researchers learn from language that would otherwise be difficult to analyze at scale. The system can be applied to a wide range of text, including customer reviews, employee feedback, patient messages, online discussions and other forms of written communication. Researchers could potentially use it to study topics ranging from consumer behavior and employee retention to public responses during major events such as economic downturns or public health crises.

For example, researchers could examine specific patterns. Are people expressing anxiety? Anger? Stress? Social isolation? Are certain concerns becoming more common over time? What real outcomes do they predict?

"Whether you have a dependent variable or not, you can see what people care about, what they talk about," Larsen said.

Not a replacement for surveys

Despite the promise of language-based research, Larsen does not see PsyProxy as a replacement for traditional surveys.

"Surveys have a unique place in the world for getting answers to highly specific questions," he said.

Instead, he sees PsyProxy as another tool researchers can use. Surveys can help answer specific questions, while PsyProxy can help researchers find patterns in open-ended responses and other large collections of text. Researchers might even use it to analyze the "anything else you'd like to add?" responses that often get overlooked.

But Larsen said researchers still need to be careful about what language can and cannot reveal.

A customer who posts an angry review may never stop buying from a company, for example. Someone who expresses frustration online may never act on those feelings.

PsyProxy measures patterns associated with concepts such as frustration, trust and social connection, Larsen said, not what people ultimately do.

Language-based approaches also come with their own biases. Just as survey respondents may tailor their answers to what they think researchers want to hear, online conversations show only part of people's lives. Social media posts, reviews and comments can reveal valuable information, but they don't always reflect the full picture of someone's life.

“Anyone who has noticed that social media makes it look like everyone else is more successful than them is already dialed into some of these biases,” Larsen said.

For Larsen, the project is the culmination of an idea that first took shape nearly three decades ago.

In 1997, he began exploring ways to turn interviews, conversations and other forms of qualitative data into something researchers could measure systematically. The vision remained largely out of reach until advances in artificial intelligence finally made it possible.

"I waited 20-plus years for language models to get good enough to use for the problem I wanted to solve," he said.

Giving researchers the 'why'

Larsen also sees the approach as an alternative to relying entirely on large language models such as ChatGPT.

While those systems can summarize text and generate responses, he argues that researchers often need a way to measure concepts across large collections of text and compare findings across studies.

As AI systems have become increasingly powerful, they have often become harder to interpret, Larsen said. Researchers may know a model is making accurate predictions without understanding why.

Larsen said PsyProxy is designed to help open that black box by identifying the psychological concepts most strongly associated with a particular outcome.

"PsyProxy is giving the power of why back to researchers," he said.

Edmondson compares the system to a translator.

"People describe their lives in ordinary words," Edmondson said. "PsyProxy helps translate those words into psychological concepts researchers can measure, test and compare."

While Larsen sees potential applications across many fields, he is particularly interested in the impact the technology could have on health and well-being.

"PsyProxy is designed to help in all social and behavioral disciplines, from sociology and psychology to marketing, but behavioral medicine is near to my heart because of the difference it can make in our lives,” he said.

The research and tool are scheduled to be presented at the American Psychological Association annual convention in August.

After nearly three decades of work, a Leeds School of Business researcher has developed a new AI tool that turns online conversations into data that can be used to study human behavior.

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