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A bibliometric analysis of mobile learning research published between 2017 and 2026 shows a sharp expansion in output. There was a big surge between 2020 and 2022 associated with pandemic-driven shifts in higher education. Mobile learning (m-learning), defined as the use of mobile devices such as smartphones and tablets to support educational activity, has grown alongside global device penetration and intensive daily usage among students, according to the study published in the International Journal of Mobile Learning and Organisation.
The researchers looked at more than 2,500 papers indexed in Web of Science, using bibliometric spreadsheet techniques and the network-mapping tool VOSviewer. This allowed them to identify patterns in authorship, citations, and research themes through statistical analysis of the academic publications.
China, Taiwan, and the USA emerged as the most active contributors, with the National Taiwan University of Science and Technology identified as the leading institution. Influential work in the field is strongly associated with scholars such as Gwo-Jen Hwang.
The team's thematic mapping indicates a shift away from early emphasis on technology adoption towards more integrated approaches to teaching. It focuses on how mobile tools can shape teaching and learning design. The analysis also highlights increasing collaboration across regions, with growing scholarly influence from East Asia and Latin America.
The team suggests that future research might take into account the advent of readily available artificial intelligence (AI) tools for learning support as well as immersive technologies such as augmented reality (AR) and virtual reality (VR). They suggest that these developments are expected to expand interactive and contextual learning experiences in higher education.
Shambare, B. and Jita, T. (2026) 'Charting the landscape of mobile learning in higher education: a bibliometric mapping of research trends and organisational contexts', Int. J. Mobile Learning and Organisation, Vol. 20, No. 5, pp.1–27. DOI: 10.1504/IJMLO.2026.154394.
This post was originally published by Inderscience Publishers and is republished here with permission.
Consider the following scenario. Suzy is 63, recently retired, and trying to decide when to start receiving Social Security and how to manage her retirement savings to minimize the tax hit.
She opens an AI chatbot, types in the details and gets a calm, well-organized and confident answer: Claim now, convert this much, here is the reasoning.
The chatbot sounds authoritative and even shows its work. So Suzy follows its guidance and never calls a financial planner. Maybe the advice was fine. But maybe it quietly ignored the fact that Suzy’s spouse is younger and in poor health, which can flip the Social Security math. It also may have overlooked that the retirement savings plan conversion it suggested would push Suzy into paying higher Medicare premiums two years later.
Suzy won’t find out for a long time, if ever, whether this guidance was right for her. And the AI will never call back to say it was unsure.
Suzy isn’t an exception. AI chatbots have entered everyday life with remarkable speed: A 2025 Pew Research Center survey found that 34% of U.S. adults and 58% of those under 30 have used ChatGPT, roughly double the share two years earlier.
A growing number are asking AI about money, and some are getting burned. According to a 2025 survey of 2,000 U.S. adults by Pearl.com, a professional services platform, 19% said they lost more than $100 by following financial advice from an AI chatbot. Among Gen Z investors, that figure rose to 27%.
These aren’t hypothetical risks. People are already paying for answers about their money that are confident – and wrong.
As a finance professor who has been closely watching the spread of AI into personal finance, this is the part of the AI story that worries me most. And it’s not the part you usually hear about.
We argue about AI the wrong way
There are two seemingly opposite complaints about AI. One is that people trust it too much, treating a chatbot like an oracle, a tendency researchers call algorithm appreciation. The other is that people don’t trust it enough and dismiss its useful tools, a tendency known as algorithm aversion.
I argue these are actually two sides of the same coin, and what decides which side you see is whether you can tell when the AI is wrong.
When an AI fails in an obvious way, you notice and lose confidence. So you’re more likely to seek a professional or another human you trust sooner than you otherwise would. That is the safe failure.
The dangerous failure is the opposite. The answer is fluent, confident – and wrong. You have no way to catch it, so you keep managing the problem yourself long past when you should have asked for help.
The trouble is that with money, the second kind of failure is the common kind.
Typical users of chatbots for financial advice tend to be younger, with men outnumbering women.Tim Gouw on Upslash, CC BY
When you mistake fluency for accuracy
Three things make financial advice especially treacherous for AI.
First, fluency is not accuracy. People naturally read a confident and well-articulated answer as competent. But how polished an answer sounds tells you almost nothing about whether it fits your situation or the accuracy of the proposed solution. A chatbot can be word-perfect and still be wrong about your taxes, because your taxes depend on details it never asked about.
But financial life is full of rare, complicated, one-time decisions: exercising stock options, understanding the alternative minimum tax, making required, minimum 401(k) distributions, deciding on a Social Security strategy as a couple, drawing up a divorce settlement.
I made a similar argument three years ago about AI trading on Wall Street. Because market crashes are rare, there’s little data for AI to learn from, so it can be most confident exactly where it is least informed.
That worry hasn’t faded. Market watchers now caution that AI trading bots are creating fresh financial risks, and that same blind spot applies to your personal finances. Researchers call this uneven competence a “jagged frontier” – reliable with common cases but unreliable for unusual ones. And in finance, the unusual cases tend to be the expensive ones.
Third, you often can’t check the work. Financial advice is what economists call a “credence good,” like a mechanic’s diagnosis or a doctor’s recommendation. You often can’t tell whether the advice was good, sometimes for years. A mistaken tax move may not surface until an audit. A bad 401(k) drawdown plan may not bite until the stock market slumps. Without quick feedback, the wrong-but-confident answer never gets corrected.
This is why the Pearl numbers above are probably an undercount, since they capture only losses people noticed.
The quiet failure is the one to watch
Notice that the real harm in Suzy’s story isn’t a single dramatic mistake. It’s that a confident answer made Suzy feel no need to call a professional, so the call never happened.
The danger is not so much that you act on bad advice but that you never seek good advice. The smoother and more reassuring the tool, the easier it is to stay in do-it-yourself mode past the point when you need outside help.
Who’s most at risk? In a study of a large robo-advising platform in India, co-author Vishaal Baulkaran and I found that its users skew young, are predominantly male and tend to be smaller retail investors and professionals. And new account sign-ups rise during periods of high market volatility.
In other words, the people leaning hardest on automated advice match that 27% figure among those Gen Zers who lost more than $100 while using a chatbot for financial advice. They reach for it just when markets turn turbulent and a wrong move is most costly.
There’s also an incentive worth naming. In my new analysis, I argue that a tool that earns its revenue by holding your attention has a reason to sound confident and helpful: Confidence keeps you on the platform. The catch is that the user it retains that way is sometimes the one who should have been handed off to a human.
A system tuned to keep you engaged isn’t the same as one tuned to protect your financial future, and the two can point in different directions. The disruption is already underway, as wealth managers face what Bloomberg has called a chatbot reckoning. A single, new AI tax tool recently sent wealth management stocks sliding as investors bet that automated advice will eat into the business.
How to be smart about using AI
These findings don’t mean that people should avoid AI for money advice. Used well, these tools are a valuable and free financial educator.
This is also not to say that a financial adviser always has the right answers. As with finding any kind of specialist, it’s important to do research first and make sure they meet the kind of criteria laid out by the Consumer Financial Protection Bureau. Fee transparency is also crucial.
But if you do turn to AI, the skill is knowing where to draw the line.
Treat AI as a starting point, not a verdict. It’s excellent for learning concepts, drafting questions and getting oriented before a meeting. It can teach people the vocabulary to have a smarter conversation with an expert.
But watch out for the signals that you have left its comfort zone and entered the territory where AI is weakest and a confident answer is least trustworthy. The red flags are large dollar amounts, tax consequences, anything irreversible and anything that turns on the specifics of your situation rather than a general rule.
Estate questions, the drawdown of retirement savings, strategies for claiming Social Security benefits, business structure and major one-time transactions all belong in this category. Those are the decisions that call for bringing in a human, such as a certified financial planner.
And remember, confidence isn’t competence. When the answer about your money sounds most polished and most certain, that’s not a reason to relax. On the hardest questions, that smooth confidence is exactly the signal that you should pick up the phone and talk to an expert.
Smartphones give humans an infinite amount of connection, whether it be through calling or FaceTime, texts, or messaging via social media apps. While being able to connect with people hundreds or even thousands of miles away is quite the superpower, it can also lead to some brain-damaging habits that affect people’s ability to focus or remain emotionally stable.
According to this recent study by Solitiared, Americans spend 1,460 hours a year scrolling on their phone, which amounts to 61 days or two months out of the year. Imagine the amount of free time people would have if they put their phones down and focused on something else! And it’s not for lack of trying, as this study points out that 70% of Americans surveyed have tried to reduce their screen time recently. It’s just very difficult to get rid of scrolling habits that are so ingrained in the brain.
Yet hope is not lost, as it’s entirely possible to break these habits and build new ones if you have a good strategy. So in this post, you can check out five different ways to reduce screen time with tips and tricks to keep you from reaching for your phone and doomscrolling.
Charts: Solitaired
1. Start Small
Try to set a small screen time goal, whether it’s staying off of one social media app for a whole day or only trying to reduce screen time by a specific increment of time, such as 30 minutes per day. You can adjust to more limits each week so that what starts as something small can make significant change in just weeks.
Rome wasn’t built in a day, and screen time certainly can’t be reduced in a day either. While small goals might seem pointless, they can help make the screen time problem seem a little less daunting, and meeting smaller goals can boost confidence and keep you on track to reduce your screen time.
2. Track Screen Time
Many smartphones allow you to track their screen time in your settings app, mapping out usage over a day, week, or month. By looking at your screen time, you can see which apps are being used the most each day and determine whether your screen time is high because you send tons of emails per day for work on your phone or because you’re spending large chunks of time doomscrolling on Instagram and TikTok. If you find yourself spending too much time on one app (or two or three!), you can target those for reduction.
Tracking screen time also offers a tangible way to hold you accountable for the smaller goals you’ve set. For example, if you decide to spend one less hour on Facebook each day, looking at your screen time for that particular app can help you stay on track. Watching your progress over time can be rewarding as you see yourself reducing the time you spend on the app over weeks or months.
3. Schedule an “Unplugging”
Most people seem tied to their phones these days because much of what we do can be filtered through our phones. You might be switching between scrolling on social media apps, texting with friends and family, or using your device for work or school. While not all screen time is a waste of time, however, being tethered to the beck and call of an incoming message or notification can make you a little too dependent on that device.
To combat this, consider an “unplugging.” It can be as simple as putting the phone away in a drawer for an hour or two or staying off your device for an entire day. Unplugging gives you a chance to spend some time off your device completely, not just off a single app. Use this time to connect with the real world in a purposeful, meaningful way. Whether you meet someone for a coffee, pick up a new hobby, relax outdoors, or just work on that to-do list that keeps growing, giving yourself a break from your phone can help you relax and begin new habits that don’t keep you attached to your phone.
4. Social Media Cleanse
One of the most talked-about approaches to reducing screen time is a cleanse. By permanently deleting social media or locking those apps so access is restricted throughout the day, you can finally resist the urge to doomscroll and focus on other things besides social media because you don’t have access to it.
There are other benefits to taking a break from social media. Social media has long been a source of depression and anxiety, with people focused on generating likes and perfect looks rather than things that truly generate joy and fulfillment. By reducing time on those apps or getting rid of the apps altogether, you can free yourself from that cycle.
5. Take on a Brain Workout
For some, scrolling on their phones is simply a way to pass the time while waiting on that email response form work or taking a lunch break. But you can still be on your phone and put your brain to work by doing a brain workout instead of doomscrolling. It can be as simple as trying a word game or playing a quick game of Solitaire. While it’s tempting to get sucked into your phone and scroll mindlessly, you can choose to engage your brain with some mental gymnastics.
Bad habits are always difficult to break, no matter what they are, and creating new habits is even harder. Thankfully, you don’t need to break the scrolling habit immediately. Starting with a small goal makes adjusting to less time on the phone a lot easier. Plus, there are other things to fill time with, such as games that work the brain or just putting the phone away and going outside. By finding the trick that works best for you, you can help yourself by scrolling less and getting that screen time down!
Job searching has never been more accessible — or more confusing. Platforms like LinkedIn, Indeed and employer career pages let candidates submit applications with just a few clicks. What happens after they click “submit,” however, has become fertile ground for misinformation.
Social media is filled with “career influencers,” resume writers, recruiters and companies promising insider knowledge of how hiring really works. Much of this advice focuses on misinformed claims about applicant-tracking systems (ATS) and artificial intelligence.
These services profit from job seekers’ uncertainty and convincing people they need specialized services, tools and products to “beat” the ATS and secure interviews.
The result is that many job seekers spend time and money following advice that has no basis in evidence. Here are four common myths about the job application process, and what the research actually says.
The statistic originated from a 2012 sales pitch by Preptel, a resume optimization company that went out of business the following year. No methodology was ever published, yet the figure has spread widely.
In reality, an ATS is software that helps employers manage applications, and its capabilities vary widely. Some systems function as digital filing cabinets, simply storing and organizing applications.
The advanced AI-powered tools are typically found in large organizations, including many Fortune 500 companies, which receive enormous volumes of applications. In Canada, most employers do not use AI in hiring, and small businesses — which employ more than 60 per cent of the workforce — are especially unlikely to rely on ATS.
Small businesses typically lack both the application volumes that make ATS worthwhile and the procurement infrastructure to adopt and maintain them.
For most Canadian job seekers, the better strategy is to focus on clearly communicating how their skills and experience match the role, and on building relationships within their profession.
Myth 2: AI can write a winning resume
A common message from career influencers is that AI can generate a tailored resume or cover letter that dramatically improves your chances of getting hired. While AI can help candidates prepare application materials more efficiently, it is not a shortcut to a stronger application.
Resume writers and career influencers claim that using an “ATS-friendly” template is essential for “beating” the ATS. Some even sell templates that promise to “optimize” your resume to secure interviews.
In reality, there is no universal ATS-friendly resume because the software employers use varies widely from one company to another. Additionally, modern ATS can extract information from common resume layouts, including columns or tables.
Their main limitation is that they are designed to process text, not images, graphics or icons. That means a clean, readable resume should be the actual target, not a template bought online.
If ATS doesn’t automatically reject resumes the way the influencer economy claims, then optimizing for a system that largely doesn’t work that way is solving the wrong problem. The real audience for your resume is a person, not an algorithm.
The better approach is to write for both systems and people. Use clear headings, relevant keywords and concrete examples that show how your experience matches the role.
In practice, this approach often comes at the expense of thoughtful job-seeking, such as identifying positions and employers that genuinely match your skills and interests, and crafting applications that reflect that fit.
AI is most effective when it enhances, rather than replaces, a candidate’s work, helping to avoid what has become known as “workslop” — a term for generic, AI-generated content.
Today’s labour market may look different, but the fundamentals of a successful job search haven’t changed much. In that sense, the best thing job seekers can do may be to ignore most of what they’re being sold.
The strongest applications are those that clearly connect a candidate’s experiences to the role, provide concrete evidence of their abilities and communicate in an authentic voice.
Research by Dr Nicholas John from The University of Manchester and Dr CJ Reynolds from the University of Copenhagen has explored the rise of ‘ragebait’ - content deliberately designed to provoke anger - and how it is reshaping the way audiences engage with morality, accountability and online behaviour.
Key insights:
‘Ragebait’ is an increasingly popular strategy for generating attention online
Content creators are engineering confrontations to provoke emotional reactions
Audiences are drawn to feelings of moral superiority and catharsis
Online ‘accountability’ is often reduced to spectacle rather than real change
The trend reflects a shift in how public shaming operates in digital culture
Why this matters
From callout videos to viral confrontations in public spaces, outrage has become a powerful currency in today’s attention economy.
Dr John’s research examines the widely viewed ‘Cart Narcs’ video series, where members of the public are confronted - and often provoked - for failing to return their shopping trolleys to storage bays in supermarket car parks.
While such content appears to promote accountability, the study argues that its real appeal lies in carefully staged conflict.
“Ragebait works because it blurs the line between entertainment and morality,” says Dr John. “It invites viewers to feel they are witnessing justice being done, while actually consuming a highly controlled and repeatable form of provoked outrage.”
Entertainment disguised as accountability
The study identifies a formula behind successful ragebait content - creators construct predictable scenarios, provoke emotional reactions, and then frame themselves as morally justified.
This allows audiences to experience what researchers describe as ‘accountability entertainment’ which stages wrongdoing and its punishment, but without any meaningful consequences beyond the screen.
Rather than encouraging broader social change, the research suggests this format focuses attention on individuals instead of systems.
“Viewers are encouraged to judge and condemn, but not to engage with the wider social conditions that shape people’s behaviour,” Dr John explains. “Accountability becomes something you watch - not something you do.”
The politics of outrage
The research also highlights how ragebait repurposes elements of callout culture – something which is originally rooted in social justice activism - into monetised entertainment. In doing so, it shifts power dynamics - instead of challenging powerful figures, creators often target ordinary individuals, amplifying their mistakes for mass audiences.
This creates what the study describes as a form of ‘atomised politics’, where collective action is replaced by individual judgement and fleeting moments of online outrage.
What needs to change
The study calls for greater awareness of how emotionally provocative content is produced and consumed, particularly as platforms continue to reward engagement-driven formats.
Understanding the mechanics behind ragebait, says Dr John, is key to recognising its broader social impact.
“Not all outrage is meaningful - if we want healthier public discourse, we need to question content that turns anger into spectacle and ask who benefits from it.” — Dr Nicholas John.
Publication details:
The research is published in Information, Communication & Society.
Google has updated the policies governing extensions in the Chrome Web Store, introducing stricter requirements for user data collection, new transparency obligations for developers, and additional restrictions on certain types of extensions.
The changes were announced on July 1, 2026, in a post on the Chrome for Developers blog.
Under the revised Limited Use Policy, any data collected must be strictly necessary for the extension's disclosed single purpose. The updated policy states that collecting user data for purposes beyond that disclosed purpose is prohibited.
Google is also expanding its disclosure requirements. Developers must now prominently inform users about all data collection, regardless of whether it is closely related to an extension's stated purpose. If an extension's data handling practices change after installation, developers will also be required to proactively disclose those changes to users.
The company has also revised its Regulated Goods and Services policy by explicitly adding predictive markets as prohibited products. As part of that change, extensions that facilitate or enable real-money transactions on predictive outcomes will not be allowed in the Chrome Web Store.
Another new policy targets extensions that attempt to bypass protections built into AI-powered services. Google said it will explicitly prohibit extensions designed to circumvent safety guardrails, usage restrictions, or other protective measures implemented by those services.
According to Google, the policy updates are intended to help maintain a trusted Chrome Web Store by strengthening data collection standards and clarifying policy boundaries related to prediction markets and AI safety. The company added that users should have clear visibility into how their data is collected and handled.
Google encouraged developers to review their existing extensions against the revised policies before enforcement begins on August 1, 2026. The company said extensions that are not compliant after that date may face enforcement action through the Chrome Web Store.
The updated Surfshark analysis reveals that instead of saying "thank you" to your chatbot, you can run the AC for seven seconds or cool down with a mini fan for three minutes.
One ChatGPT query consumes energy equivalent to running a 40W mini cooling fan for about three minutes. Similarly, a single query uses the same amount of energy as charging your phone with a 5W charger for 24 minutes. Compared with more powerful appliances, such as a 1000W single-room air conditioner, one ChatGPT query equals about seven seconds of AC use. This means you could run an AC unit for 10 minutes with the energy used by approximately 86 queries. Finally, running a regular 550W household refrigerator for one hour uses roughly the same amount of energy as 277 ChatGPT queries.
If every person in the USA made a single query to ChatGPT, it would use an estimated 685MWh of energy. To put this into perspective, this amount of energy could power approximately 63 average American homes for an entire year, given that the average USA household consumes about 10.8MWh annually¹.
Each ChatGPT query produces an estimated 4.32 grams of CO₂². This is because powering the data centers that run these queries requires electricity, much of which is still generated from fossil fuels that emit carbon dioxide. Multiplied by millions of queries daily, this results in significant carbon emissions. For instance, just one day of everyone in the US making a single query could emit around 1479 metric tons of CO₂ — roughly equivalent to the annual emissions of about 322 average gasoline cars³, or the same carbon footprint as 1,500 people flying from London to New York and back⁴.
The global number of AI users reached approximately one billion⁵ in the first half of 2026, nearly tripling from 378 million⁶ users in the first half of 2025. This represents an increase of nearly 622 million users year over year. As AI adoption grows, optimizing energy efficiency and carbon impact becomes increasingly critical.
ChatGPT’s estimated energy consumption per simple query varies across studies, ranging from 0.3 watt-hours (Wh) (Epoch AI⁷, 2025) to around 3Wh (3Wh — Alex de Vries⁸, 2023; 2.9Wh — BestBrokers/EPRI⁹, 2024). These differences reflect variations in model size, hardware efficiency, and measurement methods. This variation highlights both ongoing improvements in AI infrastructure and the complexity of accurately measuring AI energy use. For this study, we used an average of 2Wh per ChatGPT query. Comparing the 2Wh energy use per ChatGPT query with Google Search shows that ChatGPT is nearly seven times more energy-demanding than Google Search (2 Wh vs. 0.3 Wh¹⁰).
Methodology and sources
The energy consumption estimates per ChatGPT query were compiled from multiple recent studies published between 2023 and 2025. Estimates derive from lifecycle assessments and hardware efficiency models, not direct measurements, due to limited transparency from AI companies. The low estimate of 0.3 watt-hours (Wh) per query comes from Epoch AI’s⁷ 2025 analysis, reflecting improvements in model optimization and infrastructure. The higher estimate of 3Wh per query is based on earlier work by Alex de Vries⁸ (2023) and corroborated by measurements from the Electric Power Research Institute (EPRI) and BestBrokers⁹ in 2024 (2.9 Wh). Recent optimizations in GPT-4o reduced energy use to 0.3Wh, whereas older models consumed significantly more due to inefficient hardware. Equally, complex queries with very long inputs may even exceed 3Wh. For this study, we calculated the average from other studies, which resulted in a value of 2Wh.
Carbon emissions per query, estimated at 4.32 grams of CO₂², were derived from lifecycle analyses of data center electricity use, incorporating regional grid carbon intensity averages, assuming a global average grid intensity of 1.44 kg CO₂/kWh (actual emissions vary regionally, e.g., 0.144–9g CO₂/query). EPA⁶ estimates an average gasoline car emits ~4.6 metric tons of CO₂ annually (387 kg/month).
Appliance power ratings were sourced from publicly available manufacturer specifications representing typical household devices. Energy consumption over five minutes was calculated by multiplying power (in watts) by the fraction of an hour (5/60), yielding watt-hours (Wh).
