Many Teenagers Show Symptoms of Excessive Screen Use

By Felix Richter - Statista

While much of the debate around young people’s digital habits focuses on social media, screen use extends far beyond individual platforms. Between schoolwork, communication and entertainment, screens have become a near-constant presence in teenagers’ daily lives, making it increasingly difficult to separate between productive and problematic device use.

Data from a recent Eurobarometer survey suggests that this constant exposure is taking a toll. On average, EU teenagers report spending 4.5 hours per day in front of screens on weekdays and more than six hours on weekends. Many also report symptoms commonly associated with excessive screen use, including tired eyes, headaches, difficulty concentrating and sleep problems.

The findings highlight that concerns about young people’s digital wellbeing are not limited to social media alone. Instead, they point to a broader challenge: how to manage the overall volume and intensity of screen time in a way that supports, rather than undermines, health and everyday functioning.

Interestingly, 40 percent of the surveyed adolescents still see screens as a net positive for the lives of young people, versus just 29 percent who think that they have a negative impact. Among parents, screens are seen much more critically: 51 percent think that screens have a negative impact on young people, while just 17 percent think that the positives outweigh the problems.

This post was originally published on Statista and republished here with permission.

Reviewed by Irfan Ahmad.

Read next: AI can be a personal trainer in your pocket – but is it safe?
by External Contributor via Digital Information World

AI can be a personal trainer in your pocket – but is it safe?

Hunter Bennett, Adelaide University

Image: Kobe Clata - Unsplash

Generative artificial intelligence (AI) is changing the fitness industry: people can now ask chatbots to write marathon plans, build gym programs and even adjust workouts based on sleep or heart rate data.

For many, AI feels like the future of fitness coaching because it is fast, cheap and readily available.

But while AI can be helpful, research suggests it still has limitations, especially when compared with experienced human coaches.

So, let’s look at how it all works and the pros and cons.

Why are people using AI for training?

There is very little research examining exactly why people use AI for exercise programs, but researchers have offered some potential explanations.

Firstly, accessibility and cost: a chatbot can create a strength or running program in seconds without you having to wait for an appointment with an exercise professional. Not to mention these can be generated for free.

Secondly, availability. There is some research indicating people appreciate rapid feedback in real-time from AI tools. For example, you could ask an AI tool how to change an exercise due to knee pain and get a response in seconds. However, if you are following a program prescribed by a human coach, you may need to wait a day or two before discussing the issue and receiving feedback.

What are the benefits and risks?

There is a growing body of research looking at the suitability of AI-generated exercise programs across a host of contexts.

One study had ChatGPT design an individualised exercise program for five made-up people, which were then evaluated by a team of experts. They concluded the AI tool could provide safe, basic exercise recommendations, but may not provide enough adaptability to ensure long-term progress.

Similarly, another study had expert running coaches assess AI-generated running programs. They thought the exercise programs were suitable for novices but not great for trained athletes.

The effectiveness of these programs appear to be highly dependent on the level of information provided. In short, the more context you can provide regarding your current capabilities, goals and fitness level, the better the exercise program will be.

However, providing such detailed prompts requires a degree of content-specific knowledge that many people don’t have. This may make AI tools less useful to the average person.

Finally, it is not clear whether AI systems can fully account for injuries or medical conditions. Health screening is important to keep people safe before exercising and something all exercise professionals should do before writing you a program.

If this is being missed, there is the potential for an AI-generated exercise program to be unsafe for your current level of health.

Are human trainers better?

There is a small body of research comparing AI-generated exercise programs to human generated programs and the results are interesting.

One recent study randomly allocated people to one of two groups: a 12-week weight training program under the guidance of ChatGPT or a 12-week program under the guidance of a personal trainer.

There were larger increases in muscle size and strength in the personal trainer group.

Another compared a five-week AI generated fitness program to a five-week human-generated program. It found the human-generated program led to slightly greater increases in fitness and endurance than the AI program.

Finally, a third study compared a ten-week AI generated athletic performance program against a ten-week human generated program on measures of jump performance in volleyball athletes. They found the human program led to slightly greater improvements in jump distance but the same improvements in jump height.

Collectively, these studies suggest that while AI-generated exercise programs can improve your fitness, they might be slightly less effective than programs created by human experts. This may be due to their inability to provide real-time feedback and motivation.

However, it is also important to note these studies were all published in relatively low-quality journals and had some limitations. So, their findings should be interpreted with caution.

What should you watch out for?

If you choose to use AI, there are some key things to keep in mind:

• treat AI-generated programs as a starting point. Use them to organise your training, but keep in mind you might need to modify the plan if it feels unrealistic or inappropriate

• avoid increasing training volume or intensity too quickly. Sudden jumps in running distance or lifting intensity can increase injury risk, and this may not be factored into AI generated programs

• if you are completely new to a gym environment, you may want to spend a couple of sessions with a human trainer to familiarise yourself with good technique before starting your AI-generated program

• if you are looking to achieve high levels of performance, you might need to consider a human coach to maximise your progress

• be extra cautious if you have injuries, a chronic disease, or complex goals. Current AI tools may not be able to personalise your program perfectly and it might be safest to see a professional.

Hunter Bennett, Lecturer in Exercise Science, Adelaide University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Reviewed by Irfan Ahmad.

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• Explained: Google Search Caliph Problem and Why Answers Are Inconsistent

by External Contributor via Digital Information World

Teens Encounter a Myriad of Problematic Content Online

By Felix Richter, Statista

Established in 2010 by Mashable – a leading website for online culture and tech news at the time – Social Media Day is celebrated annually on June 30 to recognize how social platforms have reshaped the way people connect and communicate across the globe. What began as a celebration of social media’s connecting power has also become a good opportunity to reflect more critically on the role these platforms play in everyday life.

This is particularly relevant when it comes to younger users. As social media has become nearly ubiquitous among children and teenagers, concerns about its impact, and calls for stricter regulation, are growing louder. While platforms like TikTok, Instagram and Snapchat are central to how young people socialize, they are also at the center of an ongoing debate: how to balance the benefits of digital connection with the risks that come with it?

According to a recent Euromonitor survey conducted on behalf of the European Commission, the risks young people face online come in many forms. From misinformation and AI-generated content to exposure to sexual or violent material and the promotion of unhealthy products, lifestyles or body images – teenagers are navigating a digital environment filled with content that most parents would try to keep them away from in offline settings. Yet, many turn a blind eye to the things happening online.

“Social media can connect and inspire. But when one in three young people say it leaves them feeling stressed, sad or excluded, we cannot ignore the impact on their mental health and wellbeing,” European Commission President Ursula von der Leyen said in a statement released alongside the survey results. “And when a quarter of our young people are confronted with problematic content online, it is a clear signal that it is time for change.”

In response, policymakers in the EU and elsewhere are exploring stricter safeguards. Last year, the European Parliament proposed a minimum age of 16 for access to social media, video-sharing platforms and AI chatbots, while the EU is working on a bloc-wide age verification system.

This post was originally published on Statista and republished here with permission

Reviewed by Irfan Ahmad.

Read next:

• WhatsApp Opens Username Reservations Ahead of Feature Launch

• What 20 million bans reveal about the strain on Wikipedia’s volunteers
by External Contributor via Digital Information World

WhatsApp Opens Username Reservations Ahead of Feature Launch

Meta-owned WhatsApp announced Monday that users can begin reserving optional usernames ahead of the feature's launch later this year. The company said reservations are opening this week to give users an opportunity to secure a preferred username before the broader rollout.

In a post published on the WhatsApp Blog, the company said it is opening reservations early because its more than three billion users mean many names overlap, giving people an opportunity to reserve the username that matters to them.

Image: Whatsapp

According to WhatsApp, usernames are intended to let people communicate without sharing their phone numbers. Once the feature launches, users who enable a username will no longer have their phone number shown when messaging a person or business for the first time.

For creators, small businesses and organizations, WhatsApp said it has provided an option to claim an existing Instagram or Facebook username for use on WhatsApp.

Image: Whatsapp

Users with the latest version of WhatsApp can reserve an optional username by going to Settings > Account > Username. The company said usernames will roll out gradually over the coming months, and users will receive an in-app notification when the feature becomes available in their country.

In a separate development, WABetaInfo reported Tuesday that WhatsApp is developing a feature for Android that would allow users to link additional devices using a passkey as an alternative method alongside QR code-based device linking. According to the publication, the feature remains under development, is not yet available for beta testing, and WhatsApp has not announced a timeline for its release.

Reviewed by Irfan Ahmad.

Read next: What 20 million bans reveal about the strain on Wikipedia’s volunteers
by AI Analysis via Digital Information World

What 20 million bans reveal about the strain on Wikipedia’s volunteers

Ryan McGrady, UMass Amherst

Image: DIW - CC BY-SA

This year, Wikipedia is celebrating 25 years as the internet’s encyclopedia that anyone can edit. In its first decade, the quirky experiment for passionate nerds exploded in popularity. It became a ubiquitous information resource and a homework helper for schoolkids, much to the dismay of skeptical teachers.

In its second decade, amid the public’s growing dissatisfaction with the mangling of facts in popular discourse, it took on a new role as information infrastructure, helping categorize and validate information worldwide. Wired magazine deemed it “the last best place on the internet.” The hope was that the volunteer project could serve as the antidote for misinformation. Platforms from Facebook and Twitter to Alexa and YouTube began embedding Wikipedia material to ensure that users had context for what they read or saw.

That role has become more acute in recent years. Artificial intelligence developers have relied deeply on Wikipedia to train the large language models behind popular chatbots, which weight clean, reasonably reliable information sources more heavily than the rest of the web. Chatbots and AI-powered search engines have intensified Wikipedia’s significance, even as they siphon its readers by answering questions directly, with fewer people going to the source site itself.

But as Wikipedia’s importance – and size – has grown, the size of the volunteer corps that maintains it has not, and the number of volunteer administrators, a key moderation role, has shrunk.

I’m a researcher who studies social media platforms. I analyzed two decades of the site’s moderation records to understand the effect of these conditions. I found changes in behavior that appear to prioritize content quality while weakening the project’s ability to recruit and retain new volunteers.

Under pressure

As Wikipedia has become more prominent, its resistance to top-down control has made it a target for people who have political or financial power. There is frequent news about takedown demands and censorship abroad, investigations and threats to its nonprofit status in the U.S., and, outside the U.S., volunteers have been arrested and imprisoned.

The Wikipedia community is also sensitive to its rising importance, but not in the way you might think. Contributors are keenly aware of political rhetoric that takes aim at their project or threatens volunteers. But the chief effect on volunteers has been a sense of heightened obligation to their global readership, which has gradually increased quality standards.

As a longtime volunteer myself, I’m often taken by the community’s perseverance and the people’s desire, above all, to get on with their work of summarizing the world’s knowledge.

Wikipedia’s rich history busts myths that have risen with it.

The English language Wikipedia has maintained a reasonably steady number of contributors since 2010 – about 40,000 – yet its size and importance have grown. In 2006, it contained 1 million articles; in May 2025, it passed 7 million. A new issue is an influx of low-quality content generated by large language models.

The steady decrease in administrators is especially concerning. Administrators are a subset of trusted users, elected by the community at large, who are given powers such as the ability to delete articles or block users from editing. Unlike moderators at for-profit platforms, Wikipedia cannot simply hire more administrators. There are slightly more than 800, down from almost 1,800 in 2011, and they’re not all active.

So Wikipedia’s role has grown, but it is held together by a relatively small, shrinking community of unpaid volunteers. To keep up, the community in general and administrators in particular have had to raise their efficiency, making trade-offs between maintaining open participation and raising article quality. These trends and their costs are well documented. They are clearly visible in one of the basic administrator routines: blocking.

Shown the door

Blocking is when an administrator determines that a user is so detrimental to the project that they must be prevented from making any further edits. The blocked user can still read Wikipedia, but cannot change it.

Unlike the opaque moderation systems at the large internet platforms that I normally study as a researcher, such as YouTube or TikTok, nearly every administrative action on Wikipedia is recorded in a public log. I used these logs for a study analyzing all 20 million blocks made on the English language Wikipedia over the past two decades. I looked for patterns in frequency, duration and reasons for a block. I also assessed whether those patterns corresponded to the growing trade-offs between openness and quality.

I found that the frequency of blocks has risen sharply in recent years due to administrators using bots to preemptively block proxies. Proxies are services such as virtual private networks, or VPNs, that people use to conceal their identity, often to facilitate abuse or manipulation on Wikipedia. One of these bots, ST47ProxyBot, was so active that it accounted for the most blocks in the site’s history. Preemptive proxy blocking likely prevents damage, but it can also occasionally stop good-faith contributors. Given the increasing popularity of AI agents and their disruptive potential, this practice is likely to continue to expand.

I then removed proxy blocks from the analysis so I could focus on humans who were blocked and why. In the early years, administrators made the majority of blocks for vandalism: intentionally bad or nonsensical edits. That has shrunk to about a quarter of all blocks today. Blocks have risen for promotional editing and for sockpuppetry — when one person creates multiple accounts to manipulate content. These shifts speak to Wikipedia’s increased prominence as a target for influence.

Signs of stress

What I found most interesting was administrators’ greater use of generalized reasons for blocking, such as “disruption.” Wikipedia defines disruption as “a pattern of editing that disrupts progress toward improving an article or building the encyclopedia.” But citing this can mean nearly anything seen as counterproductive. The trend is partly explained by “disruption” being in a list of boilerplate rationales that administrators can choose from instead of entering a customized reason.

But it’s also the kind of trend I would expect to see in a labor force stretching to keep up. Administrators don’t act arbitrarily, and their actions are publicly logged and closely scrutinized. A loss of trust leads to an administrator losing their position. But to be effective, general explanations for blocks rely on shared understandings that new users may not have. Research on blocked users shows that when a sanction feels vague or unfair, volunteers are more likely to walk away – or dig their heels in – rather than reform. Good for efficiency; bad for bringing new users into the fold.

Blocks are also lasting longer on average. That, together with preemptive blocking and generalized rationales, suggests that the volunteer community is increasingly prioritizing prevention, efficiency and content quality over efforts to rehabilitate new users.

And the work is not spread evenly among the roughly 800 administrators: For many years, the most active 10% of administrators have made about 80% of the blocks. That high number dropped to 37% in 2024, largely due to changed activity by a single prolific administrator.

Bearing the cost

Wikipedia’s openness is part of how its volunteer community grew in the first place. Now that Wikipedia has become infrastructure, that community is rationing openness to preserve quality for readers. If Cory Doctorow’s zeitgeist-capturing idea of platform “enshittification” is fundamentally about ruining the experience of end users for the sake of the shareholders, Wikipedia is attempting something like the opposite. The end-user experience is being preserved, and the people behind the scenes are bearing the cost.

Wikipedia has adapted remarkably well in its evolution from early web experiment to one of the most important global sources of information. The open question, for a resource that so many humans – and now machines – rely on, is how long the volunteer system can keep enduring the cost.

Ryan McGrady, Senior Research Fellow, Initiative for Digital Public Infrastructure, UMass Amherst

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Reviewed by Irfan Ahmad.

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• Research Finds Faster Replies Improve Hiring Prospects When They Appear Authentic in Online Marketplaces

by External Contributor via Digital Information World

Research Finds Faster Replies Improve Hiring Prospects When They Appear Authentic in Online Marketplaces

Many people worry that responding too quickly to a prospective employer could make them seem too available or even desperate. New research published in Management Science suggests they may be worrying about the wrong thing.

Image: Jonas Leupe - unsplash

Analyzing 11.6 million marketplace interactions and a series of experiments involving both job candidates and service providers, the researchers found no evidence that delaying a response improves hiring prospects. Instead, employers consistently preferred faster responders.

"People have this intuition that playing hard to get is somehow useful," said On Amir, a professor at the UC San Diego Rady School of Management and co-author of the study. "We find the opposite is true."
Speed matters because it signals responsiveness."

The study combined real-world marketplace data from Fiverr, a platform connecting employers with freelancers, with three main experiments involving more than 3,600 participants and five supplemental studies involving another 5,000 participants.

In the Fiverr data, a one-hour delay was associated with a 46% reduction in hiring likelihood, while a full-day delay reduced hiring likelihood by roughly 90%.

The effect persisted even when participants had access to other information, including ratings and the content of a response.

The experiments suggested that faster responders made better first impressions. They were judged to be warmer and more competent – and most importantly, as likely to be more responsive in the future.

People use reply speed, the researchers conclude, to infer what someone might be like to work with.

"Speed is a signal. People see a quick response as a sign that you'll be attentive to their needs in the future, not just right now," said co-author Einav Hart of George Mason University.

Interestingly, the researchers found a gap between what people said about response speed and what they did. Participants reported that same-day responses would be just fine, yet consistently preferred much faster responders when making their actual hiring decisions.

Authenticity matters, too

The researchers caution against reducing the findings to a simple rule about replying as quickly as possible.

"Speed matters because people use it as information," Amir said. "But there isn't an equal sign between speed and responsiveness. Authenticity matters, too."

The researchers found that while response speed influenced hiring decisions, people also paid attention to whether a response appeared personalized and attentive.

That distinction may become increasingly important as AI makes instant responses easier to generate. While automated replies can eliminate delays, they may not convey the thoughtfulness or engagement that people ultimately value. In the experiments, faster replies lost their appeal when recipients believed them to be generated automatically or by an AI.

The takeaway is straightforward: Once someone reaches out, there appears to be little advantage in making them wait. But a quick response is most effective when it is also genuine.

Read the full study, "Speed Is a Signal: When Faster Replies Increase Hiring Likelihood."

In addition to Hart and Amir, other co-authors of the study are Eric VanEpps of Vanderbilt University and Ovul Sezer of Cornell University.

This post was originally published by the University of California San Diego Today and republished here with permission.

Reviewed by Irfan Ahmad.

Read next: NUS research reveals how parenting styles influence children’s honesty
by External Contributor via Digital Information World

NUS research reveals how parenting styles influence children’s honesty

By National University of Singapore

Parents who come down hard on their children for telling lies or misbehaving may believe that they are teaching the child right from wrong. But new research by NUS suggests that both overly strict or punitive parenting could be part of what drives the behaviour in the first place.

Drawing on two long-term studies of Singaporean families, researchers from NUS Psychology found that ‘authoritarian parenting’ and ‘harsh punishments’ were associated with greater dishonesty in children across early and middle childhood. The studies suggest that this is not out of defiance, but a way for the children to cope with self-criticism, the pressure to perform and the fear of making mistakes.

The first study, published in the academic journal Child Development, tracked preschoolers and found that those whose fathers were stricter and enforced rules with little explanation were more likely to cheat later on. The researchers observed that these children also tended to be harder on themselves.

The second study, published in Developmental Psychology, followed school-going children over three years and found that children subjected to physical punishment like spanking were more likely to cheat and lie over time.

The studies were led by NUS Psychology’s Associate Professor Ding Xiao Pan and doctoral student Ms Liwen Yu. The second study was also led by Associate Professor Ryan Y. Hong from NUS Psychology.

Authoritarian parenting promotes cheating through self-criticism

The first study examined 479 families who participated in the Growing Up in Singapore Towards Healthy Outcomes (GUSTO*) birth cohort study, one of Singapore’s largest and most comprehensive birth cohort studies.

Researchers assessed parenting styles via a parental questionnaire when children were four and a half years old and measured cheating behaviour a year and a half later using a dart game.

The study found that 61 per cent of children cheated, with strict paternal parenting at age four and a half years significantly predicting this behaviour.

“Authoritarian parenting is characterised by high control, low warmth and harsh discipline without explanation. While parents may believe this approach instils discipline, our research shows it may actually undermine children’s internalisation of moral values,” said Assoc Prof Ding.

Researchers found that children’s self-criticism helped explain this link. Children with stricter and more controlling fathers were more self-critical in a sketching task done as part of the study, which predicted a greater likelihood of cheating.

“Self-critical children may feel intense pressure to maintain a flawless image and cheating becomes a maladaptive coping strategy. It is a way to avoid feelings of inadequacy and secure external validation," Ms Yu explained.

“To our knowledge, this is the first study to investigate the developmental mechanisms linking a discipline-oriented family environment to cheating behaviour,” she noted.

Harsh punishment breeds deception in school-going children

The second study followed 302 Singaporean families with school-going children aged seven to nine years, examining whether negative parental control predicted children’s deceptive behaviours over time.

Negative parental control comprises harsh punishment, discipline and ignoring. Of the three, only harsh punishment, which includes physical punishment like slapping and spanking, was found to increase children’s lying and cheating over time.

Harsh parental punishment at age seven significantly predicted increased deceptive behaviour at age eight, with this pattern continuing into age nine. The relationship also worked both ways: children’s deceptive behaviour at age eight predicted harsher parental punishment at age nine, suggesting a troubling cycle.

The study also identified children’s dysfunctional attitudes, like believing they must do well to be liked, as an important pathway linking harsh punishment to dishonest behaviour.

“Children exposed to higher levels of negative parental control were more likely to internalise dysfunctional beliefs such as ‘I have to do well to be liked’ or ‘I shouldn’t make mistakes’. They may then resort to lying to meet these unrealistic expectations or avoid further punishment,” said Ms Yu.

Cultural context and practical implications

Singapore is a useful setting for the studies because strict, obedience-oriented parenting and physical discipline remain relatively common.

However, even in Singapore, where authoritarian parenting is more culturally accepted, findings suggest it still poses risks for children’s moral development.

“What both studies reveal is that strict parenting doesn’t directly cause dishonesty. Rather, it changes how children see themselves, and it’s this altered self-view that leads to cheating and lying,” said Assoc Prof Hong.

The research team acknowledges that dishonest behaviour in children is multifaceted and influenced by cognitive development, social factors and individual differences. However, these studies provide crucial evidence that parenting practices play a significant role during critical developmental periods.

Ms Yu said, “Understanding these developmental pathways is essential for designing effective interventions. Rather than responding to children’s dishonesty with harsher punishment, which our research shows may actually worsen the problem, parents and educators need to address the underlying psychological mechanisms.”

This post was originally published by the National University of Singapore (NUS). It has been edited for style and length and is republished here with permission.

Image: Jhonatan Saavedra Perales - Unsplash

Reviewed by Irfan Ahmad.

Read next: ‘Alexa, tell me a joke’: how talking to AI impacts young children’s development
by External Contributor via Digital Information World

‘Alexa, tell me a joke’: how talking to AI impacts young children’s development

Clara Macarena Ponce Romero, Universidade de Santiago de Compostela

Image: Vitaly Gariev - unsplash

Children are innately curious, and throughout any given day they come up with all manner of questions: Why don’t fish have hair? Why do flowers wilt so quickly? Their need to understand the world – and develop their language skills and ideas – makes them tireless conversationalists.

While their inquiries would usually be directed at parents or teachers, in modern homes even the youngest kids might now talk to a digital interface like Siri or Alexa. These AI systems are fast becoming part of many children’s everyday lives, as kids ask them to play music, help with their homework, answer questions, or just chat to them.

These kinds of interactions are no longer strange, but we need to ask what happens when they become completely routine. Do they change the way children learn to communicate? Do they change the words they use? And are they a threat to kids’ cognitive abilities?

Language learning

Learning to speak has never been a question of just learning words. Children acquire language through human relationships, and by building emotional ties to other people. They learn to take turns, how to interpret silence and context, and how to tell when someone is tired, annoyed or distracted. They also discover that conversations do not need to be perfect – there will always be interruptions, misunderstandings, and off-the-cuff explanations.

But AI does not think like a human. Think about your interactions with ChatGPT or Gemini. We rarely lose our patience while talking to these virtual assistants, partly because these interactions are, by their very nature, governed by a very different logic to human conversation. These tools are built for quick responses and infinite patience, and this changes the experience of communication.

AI and politeness

In many homes, something very curious is becoming increasingly common: some children (and even adults) are adapting their speech so that virtual assistants will understand them better. They speak in simple sentences, and give direct instructions: “play cartoons, open YouTube, tell me a joke”. This kind of speech – known as instrumental language – aims to get immediate results.

This shift does not necessarily mean children are becoming ruder or less empathetic, but it may influence their expectations of conversation in general. Human interactions are usually slow and ambiguous, and require patience, attention and negotiation. Chatbots, on the other hand, are designed to give quick, fluid responses, or even create a sense of virtual empathy with the user.

This all leads us to a question that may seem minor, but reveals a lot: should we teach children to say “please” and “thank you” to Alexa? Beyond the surface-level question of whether we should be polite to machines, this debate forces us to think about the communicative habits that children develop through daily interactions with technology that always obeys them. The wider question for families and educators alike is: what idea of “conversing” will children construct in this context?

Alongside these doubts, we should not lose sight of the opportunities that these systems present. Many children feel freer to ask questions when they do not fear judgement, and a chatbot will repeat an explanation as many times as is necessary, adjust the level of complexity, or support them as they learn new languages or concepts.

These tools provide a safe space for trial and error, free from the social pressures that often accompany human conversation. This is not just the case for children. Many of us now resort to AI to ask ordinary questions, from “Alexa, how do I recover my password?” to more embarrassing queries that we would rather not voice out loud.

Responding is not understanding

Current AI systems produce extremely convincing answers, but they do not understand the world the way a person does. They do not have experiences, emotions, or intentions – even if they talk like they do. Just like many adults, young children tend to attribute human qualities to the things they interact with. If something can converse, it is easy to presume that it also has understanding or knowledge.

However, a lot of the information in human conversation is unspoken. An adult can tell when a child’s question is the product of curiosity, fear, or a simple need for attention. This pragmatic dimension – consisting of gestures, tone, looks, feelings – is crucial for children’s development. It is difficult to replicate in a machine, which can only offer an answer without capturing any of this nuance.

Humans are not machines

When children grow up surrounded by a particular kind of linguistic exchange – one that consists of quick responses and having every single request obeyed – it ends up shaping habits, expectations and ways of interacting. This can lead children to always expect clear, quick, effortless answers, as though any conversation were something to be resolved on the spot.

The adults who live with young children therefore have a vital role to play. They are the ones who mediate daily use of these tools both at home and at school, who understand their limitations, and who are able to integrate these conversations into general learning.

A child asking Alexa to answer their questions or tell them a joke is not, in and of itself, detrimental to their language development. But we should guide these conversations so that they understand they are dealing with a machine that responds to them, not a person.

We need to show children what separates us from machines, how we should interact with them, and in what situations it is alright to use them. We should accompany them in these everyday interactions, commenting on them and helping them to understand AI’s limitations.

AI can be useful as a support, but under no circumstances should it take the place of replace conversation between people. Despite rapid developments in technology, human interaction remains at the heart of the way we exist in the world.

Clara Macarena Ponce Romero, Profesora del área de Didáctica de la Lengua y la Literatura, Universidade de Santiago de Compostela

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Reviewed by Irfan Ahmad.

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• Research Finds People Better Understood Literal Than Figurative Cybersecurity Language

by External Contributor via Digital Information World

Research Finds People Better Understood Literal Than Figurative Cybersecurity Language

By Flinders University

Cyberattacks now cost the global economy trillions, yet most people still struggle to understand what actually happens when a breach occurs.

Image: freepik

Research by Associate Professor Sky Marsen, an applied linguist and Communications course director at Flinders University, and Professor Robert Biddle, a computer scientist based from Carleton University, Canada, suggests a surprising reason for this gap: the language used to explain cybersecurity may be part of the problem.

In an experimental study comparing “figurative” cybersecurity language (terms such as phishing, virus, or trojan) with more literal explanations, the authors found that people understood incidents significantly better when the language was clearer and less metaphorical.

This challenges a widespread assumption in science communication – that metaphors help non-experts grasp complex ideas. In cybersecurity, the opposite may be true.

“These terms weren’t designed for the public in the first place,” explains Associate Professor Marsen. “They emerged from inside hacker culture, and terms that may sound creative and playful within expert communities, are often opaque to outsiders. When they are used in public communication, they can obscure rather than clarify what’s happening.”

Given the rise of cybersecurity concerns, Associate Professor Marsen says it’s timely to understand how non-experts understand cybersecurity words and metaphors – especially the figurative language created by computer scientists to describe cybersecurity incidents.

A lack of accurate information makes cybersecurity an issue that is difficult to clearly explain to the public – and this can lead to major losses for individuals and serious reputational damage for organizations.

“Organisations routinely tell customers they’ve been hit by phishing or a malware attack, but if people don’t fully understand what that means, they may not know how to respond or protect themselves,” says Associate Professor Marsen. “Worse is that unclear communication can downplay the responsibility of organisations, or leave users vulnerable.”

Using a set of cyberattack stories composed with figurative words and a set composed with more literal versions, and an online survey, the study examines whether the use of metaphor and neologism clarifies or obfuscates the technical aspects of cybersecurity for non-experts.

The results showed participants in the literal set scored significantly better in comprehension. However, participants made important errors in both literal and figurative versions. This underlines the need for organizations to employ language strategically and provide more effective explanations of cybersecurity situations.

Associate Professor Marsen says a key takeaway from this research is that paying attention to language choices in professional communication is not just a stylistic choice but a public safety issue.

The research – “Grok hackspeak? Communicating cybersecurity with figurative language”, by Sky Marsen and Robert Biddle – has been published by the International Journal of Business Communication. https://journals.sagepub.com/doi/10.1177/23294884251329160.

This post was originally published on Flinders University News and republished here with permission.

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World

85% of kids are still using social media despite ban. But we need a new measure to judge its success

Samuel Cornell, The University of Queensland

Image: Sixteen Miles Out - unsplash

Six months on from Australia’s under-16s social media ban taking effect, the early verdict from headlines and children themselves has been blunt: it isn’t working.

A new study published today in the British Medical Journal appears to add even more weight to this judgement.

Led by University of Newcastle public health researcher Courtney Barnes, the study found very little evidence that kids had stopped accessing restricted social media platforms such as TikTok, X, Facebook and Instagram.

But the question “are children evading social media age checks?” might be the wrong one to ask when considering the long-term success of Australia’s world-first experiment.

Isolating the effect of the ban

The team behind the new study followed 408 adolescents aged 12–16, surveying them just before the law took effect in December 2025 and again three months later. They compared teenagers just under the age cutoff with those just over it to isolate the law’s effect.

They found more than 85% of under-16s were still using restricted platforms at follow-up, mostly through their own accounts.

Two thirds had encountered age verification, but the most common form was simply being asked to state their age. A minority used fake accounts or private browsing to access social media. But VPN use to evade the ban was rare.

When the researchers checked whether under-16s used social media any less than the just-over-16s who were free to keep their accounts, they found no meaningful gap at the age cutoff.

The researchers were transparent about the study’s limitations. The analysis was underpowered (which means the study may not have had enough participants to detect an effect if one existed). The sample sizes either side of the cutoff were also small.

Nevertheless, these results square with recent research from the eSafety Commissioner that showed roughly 7 in 10 children kept their accounts after the law came into effect.

So, case closed, right? The ban is a failure? Not quite.

An unrealistic pipe dream

It was an unrealistic pipe dream that the ban would stop all of today’s under-16s from using social media overnight. All online technology comes with inherent capacities to be exploited or its features circumvented.

Instead, the ban enables the government to put pressure on social media companies to comply with their directives – to restrain and contain them with greater power than existed before.

The ban should be considered over a longer timeframe. Its logic is more aligned with another form of public health law: the generational approach now being applied to tobacco control.

Britain’s Tobacco and Vapes Act, which received royal assent in April 2026, bars anyone born on or after January 1 2009 from ever being sold tobacco.

The aim is not to make today’s smokers quit but to raise a generation for whom smoking never becomes normal. Australia’s social media law makes a similar bet: that if access is delayed long enough, social media might lose its grip on childhood the way cigarettes slowly did.

That is the measure that matters, and it’s a far slower and less certain test than counting how many teens still have Instagram six months after the ban took effect.

A benighted idea for future generations

Granted, there’s a catch to this framing.

Tobacco use has been denormalised with a public health approach for decades, and its supply has been squeezed from multiple directions: higher prices, plain packaging, advertising bans.

It’s hard to put pressure on social media use in the same way. Effectively, social media is “free”, practically infinite, and engineered to maximise engagement.

Shifting a generation’s social media norms this way only works if the pressure on platforms is relentless and sustained for years, not abandoned the moment the first headlines call it a failure.

My research into social media use and risk-taking found the same difficulties: norms are sticky. Social media rewards risky content and changes what is deemed as normal or acceptable. Changing norms like these overnight is unlikely.

But viewed in the long term, or even generationally, we can see how social media use for children may become a benighted idea for future generations.

Effects not clear for a decade

Naturally, laws that “ban” things often have unintended or even detrimental consequences. When mandatory bicycle helmet laws were introduced in Australia in the early 1990s, one result was that some people simply cycled less.

The new study in the British Medical Journal reflects this, with small numbers of young people turning to fake accounts, private browsing or messaging apps. Some may drift to less visible corners of the internet that are harder to watch than the mainstream platforms.

We shouldn’t take this to mean the ban is a failure. It means we are judging it on a timeline that does not fit its design.

The researchers make the point themselves: the greatest opportunity may lie with children under eight who have not yet started using social media, rather than teenagers whose habits are already set, whose norms are to use social media.

By their estimate, the full effects may not be clear for a decade.

Australia has volunteered to be the world’s test case, with other countries now following. To do the social media age restrictions justice, we should test the right thing.

Samuel Cornell, Honorary Research Fellow in Public Health, The University of Queensland

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Reviewed by Irfan Ahmad.

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by External Contributor via Digital Information World

How AI Is Supporting the Next Generation of Small Businesses

By Jelyn Johnson

Artificial intelligence is not something that is used occasionally to help small businesses. It is now a part of how these businesses work every day. Research from Adobe shows that artificial intelligence is becoming a part of what makes small businesses run. It is no longer an extra tool that helps with a few things but it is now a key part of everything from marketing, to the way things are done in the office.

This is a deal: artificial intelligence is not just making things a little faster, it is actually changing the way work is done in small businesses. It is changing how all the different parts of a business work and operate with each other. AI is really changing what it means to be a business.

A New Formula to Help Business Growth

Small businesses software often has a specific application. There is one software application for sending emails, there is another one for accounting purposes, and another for designing. The role of AI in small businesses is breaking this trend. The majority of small businesses are embracing AI in their operations for more than one process compared to their previous use for individual processes.

The truly amazing thing about all of this is the fact that the use of software is gradually turning into the integration of software into operations. The AI technology is being applied to automate repetitive operations such as creation of content, media posting, customer communications, and handling administrative tasks.

AI software is not being used alone. Rather it is being added to the existing ways of doing things to make everything run more smoothly. AI technology is being used to complement tasks like content creation, social media creation, customer communication and administration to make these tasks easier for businesses.

This is a significant change in small business operations since small businesses are always looking for ways to save time or be more efficient.

Workflow Automation is Becoming the Primary Driver of Adoption

Perhaps one of the most evident pieces of evidence from research results is the fact that small businesses are not using AI as an experimental tool. The reason why AI adoption among small firms is happening is practical; it means that people want to save their time by making work more efficient.

There were tasks that used to take much time, such as creating social media posts, marketing copywriting, or anything else, which is done for the customers’ benefit. These kinds of tasks are now often partly or completely automated using the power of AI. Often, these processes are not done from scratch anymore, but instead, they are handled as a kind of first draft in collaboration with AI assistance.

This trend is really important because it changes the way people work at businesses. Artificial Intelligence is not taking the place of workers, it is just doing repetitive tasks.

Time Savings are Being Allocated to Higher-Value Work

We can see that when small businesses use Artificial Intelligence they do not get free time. Instead the owners of these businesses are using this newfound time to do things that will help their business continue to grow.

The time saved is being used for thinking about the future, talking to customers and making their business better. AI is helping business owners to focus on these things. This implies that AI technology is not only increasing efficiency and also changing the priority of time usage.

The reallocation serves a very important structural change in the operation of small businesses. As the repetitive work is reduced and time is saved, the constraint moves from being ‘available time’ to ‘quality of decision-making’. The business owner is no longer constrained by their ability to execute as much as they can be by the effective use of time.

• Also read: AI saves time – so why does it make us feel guilty?

AI is Quietly Standardizing Small Business Workflows

Another developing trend is the process standardization that occurs within small businesses as they begin to implement the use of AI at scale. With many companies implementing tools that are used to generate content, to communicate, and to manage marketing processes, there are certain patterns of behavior that start to develop.

For instance, the content production process is often becoming increasingly uniform in its pattern, with the development of an idea, automatic generation of the draft, and finally, manual refining of the draft.

Over time, this will lead to a smaller variation of actions taken by the small businesses when performing the tasks, especially marketing and communications tasks. At the same time, it raises the question of differentiation, as all outputs from the use of AI become similar.

How Gains in Productivity are Beginning to Show Their Impact

From our findings, AI positively contributes to business operations on top of streamlining day-to-day processes. Productivity increases lead to drastic improvements on business outputs.

There are observable improvements among small businesses as they deploy artificial intelligence. They are improving their core competencies, which in turn has a positive impact on customer engagement levels and the bottom line. This shows that AI has an impact beyond internal business operations for small businesses.

Customer engagement and revenue generation are other ways artificial intelligence has a positive impact on business performance.

The main process behind this effect seems to be compounded efficiency. The time savings in the execution process allow the company to do more work in terms of marketing, customer communication, etc., without additional costs.

In that way, AI acts not as a device but as a multiplier of business potential.

The Emerging “AI-first” Small Business Model

Together, these developments seem to signal the emergence of a new operational model, the small business that operates on the basis of AI-first. This does not imply that small businesses become fully automated operations.

Decision-making methods will still be a collaborative effort led by human thinking when it comes to strategy development, creative aspects, and relations with customers. Yet, the process of performing the tasks associated with decision-making is done through the assistance of AI algorithms.

Overall, this results in a combined operational model in which humans make decisions, and AI performs the majority of tasks.

Thus, AI becomes a tool that expands the capabilities of an existing business operation.

Take a look at these infographics for more insights on small businesses are leveraging AI today:

Some key insights:

• AI usage has climbed to 85% among surveyed small business owners, with 65% citing improved confidence in future growth.
• "Small business owners using image generation tools are 32% more likely to cite burnout reduction as their primary motivator".
• "47% of small business owners saw an increase in revenue since beginning to use Al tools, with an estimated revenue increase of 21%".
• "Small business owners are investing an average $218 on Al training this year to stay competitive, with about one in seven investing $1,000 or more".

The Next Phase of Technology

The more that small businesses use AI technology the harder it will be for other small businesses to keep up. When small businesses use AI technology they can do things without needing to hire more people.

For example AI technology helps with a lot of tasks. This means small businesses can be more productive and efficient with their time. The notion of what software and tools can do for businesses is also changing.

It's not just one thing, people are looking for systems that can automate a lot of things. It's the AI technology that makes all these systems work together. It makes things run smoothly, which in turn makes things happen faster.

If you look at the landscape you can see that AI technology is a part of how small businesses operate online. AI technology is transforming how small businesses do business on the Internet.

Thus, what we can conclude from this data is that the use of AI tech in small businesses is no longer a process of experimenting or making the process more efficient. It becomes an essential part of the change in how small businesses operate.

Reviewed by Irfan Ahmad.

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The Highest-Earning Creators of the Internet Content Machine

By Katharina Buchholz, Statista

Content creator MrBeast aka Jimmy Donaldson earns more than anyone else in the business. This is according to the newest edition of the Forbes Top Creators list published Tuesday. The 28-year-old who grew up in North Carolina made $300 million in gross earnings between March 2025 and March 2026, according to the source, far outpacing other influencers. Donaldson has had resounding success with his YouTube channel focused on over-the-top challenges (and the occasional grand gesture) and released the second season of his Amazon Prime series Beast Games in January. Earnings also come from a food side business, an analytics tool and toy and clothing licensing. Donaldson's company reportedly has taken on venture investments at a $5 billion valuation as well as purchased a personal finance app for teens.

The latest release of Forbes' list of the most successful influencers shows that YouTubers generally rank high among the best-paid content creators. One aspect of this is sponsored posts and ads earning more if they are in a video format. According to Forbes, Donaldson is in fact capitalizing on this aspect. However, many creators who have earned millions as social media personalities have done so by outside business deals. Rhett McLaughlin and Link Neal of channel Rhett & Link have branched out from YouTube sketch comedy and other entertainment content to streaming deals, live appearances, merch and book sales. Mark Edward Fischbach, known as Markiplier on YouTube, initially uploaded gaming videos, but now also earns cash with merch sales and a clothing line. After some podcast and TV deals, he self-released his first feature film earlier this year.

Also among the highest earners are two creators focusing on personal finance and business tips. Phoenix-native Codie Sanchez pivoted from a career in journalism and finance to teaching small business ownership through her multiple online channels, a podcast and a New York Times Best Seller. She has been a prominent voice in the passive income and vending machine/laundromat hype has has been circulating online. Serial startup founder Steven Bartlett meanwhile became famous for his podcast The Diary of a CEO, which streams on YouTube and audio platforms. The format, which features interviews with CEOs, entrepreneurs and celebrities, became one of the most listened-to podcasts in the world. Since then, Bartlett has continued founding and investing in companies, has authored two books and has become an angel investor on the British TV show Dragon's Den.

Second-ranked Dhar Mann shot to internet fame producing mini dramas that can be viewed on YouTube and social media platforms after trying his hand at other entrepreneurship ventures. Having started out with motivational content, his current video releases have been described as morality plays, featuring stories in which a character has to navigate between good and evil influences. Raised in Oakland, Calif., by Indian parents, Mann employs 200 studio crew who shot video in eight teams simultaneously and collaborates with around 2,000 actors per year.

This post was originally published on Statista and republished here with permission.

Reviewed by Irfan Ahmad.

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Spy agencies say AI can help combat AI cyber risks. But don’t forget the basics

Toby Murray, The University of Melbourne

Cybersecurity agencies of Australia, Canada, New Zealand, the United Kingdom and the United States issued a call to action on Monday for cyber defenders. The message was clear: artificial intelligence (AI) is a powerful weapon for cyber attackers; defenders must act urgently to improve their cyber defences.

Image: Trophim Laptev/Unsplash

There is much hype and uncertainty surrounding AI and cybersecurity right now. This latest statement comes little over a week since the US government caused frontier AI provider Anthropic to block access to Mythos and Fable, its most advanced AI technology, over fears they might be misused by foreign adversaries to attack US government systems.

In this torrid environment, it’s important for cyber defenders to look past the noise and prioritise what is truly important in protecting their systems.

A call to arms

The joint statement was issued by the heads of the national cybersecurity agencies of the Five Eyes. It warns that AI is dramatically shifting cyber risk and spells out how defenders must act to secure their organisations.

It notes how powerful AI is already helping adversaries carry out more sophisticated attacks more quickly.

One way this is happening is through automated vulnerability discovery and exploitation. No software is perfect. Adversaries leverage subtle design or implementation flaws in a system’s software to break into that system. They then take control of it and use it as a staging ground to launch further attacks.

This is why it’s so important for cyber defenders to keep up to date with deploying software patches. These are small modifications to system software that close off known vulnerabilities.

AI is enabling adversaries to find flaws orders of magnitude faster, as well as to work out how to exploit those flaws to carry out attacks.

For this reason, the Five Eyes statement warns that AI is dramatically shrinking the time between when a vulnerability is first discovered and when it is first exploited in an attack. Defenders can no longer afford to wait weeks before deploying software patches.

What can defenders do?

The Five Eyes report notes cyber fundamentals are crucial and encourages organisations to use AI to boost defences. But deploying AI without first investing in cybersecurity basics would be a mistake.

The cyber defenders who will be able to weather the AI storm will be those who already have mature practices. They know exactly what assets they need to protect, which systems in their organisation are exposed to attack, and what defences are in place to protect exposed systems. They also know to measure defence effectiveness and determine where defences are missing.

They also use evidence-based processes for tracking known vulnerabilities in their systems and prioritising which are most important to patch. These are backed up by reliable processes for rapidly testing and rolling out software patches, as well as for responding to cyber breaches and incidents.

When AI makes finding software vulnerabilities cheap, the next generation of software needs to be engineered to be secure by construction.

Working out the best methods to do this is what I have devoted my research career to.

Before reaching for AI, defenders should first invest in their fundamentals. Otherwise, they are effectively deploying a robot guard dog to defend an unlocked door.

The role for AI in cyber defence

This doesn’t mean AI can’t play an important role for cyber defence – just that it should augment rather than replace strong cyber fundamentals.

AI benefits attackers and defenders alike. An AI model that can help attackers find software vulnerabilities can also help defenders fix those same vulnerabilities.

AI that can automatically exploit software vulnerabilities is just as useful to defenders in helping them to confirm their software has been correctly patched. AI that can map and discover sensitive assets within a computer network is useful for both offensive and defensive purposes.

This is why it’s so important that defenders have access to AI capabilities, so they can be leveraged to harden and protect systems before that same AI is used to attack them.

Can regulation help?

Working out how to balance the competing benefits and risks of new cybersecurity technology is nothing new.

In the 1990s, society grappled with how to regulate the encryption that protects online communication from adversaries but also allows them to avoid law enforcement.

In the 2000s the rise of cyber exploit kits allowed defenders to better test their systems but also enabled any disaffected teenager with an internet connection to become a “script kiddie” hacker, leading to arms controls debates a decade later.

The 2010s gave us blockchain technologies such as Bitcoin and other cryptocurrencies, which were built on defensive cyber technologies but whose lasting legacy remains the rise of ransomware attacks and online illicit marketplaces.

The rise of AI presents a similar dilemma for regulators.

A blanket export ban on advanced AI models is likely to be counterproductive. Open-source AI models such as DeepSeek lag only months behind the most advanced models of OpenAI and Anthropic. Recent research suggests that much of that gap can be closed by pairing less powerful AI models with complementary technologies.

Defenders should therefore assume their adversaries already have access to AI on par with that used for cyber defence. Only by investing in strong foundations can they hope to escape the cat-and-mouse AI cyber arms race.

Toby Murray, Professor of Cybersecurity, School of Computing and Information Systems, The University of Melbourne

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Reviewed by Irfan Ahmad.

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