AI can help with academic research but doesn’t always get it right – here are some tips to check the information you receive

By Allison Faix, Coastal Carolina University and Samantha Duncan, Coastal Carolina University

Fact-Checked by Irfan Ahmad.

Image: Kaptured by Kasia - Unsplash

Almost 25% of adults in the United States are using artificial intelligence chatbots on a daily basis, a 2026 Pew Research Center study found.

At the same time, another recent study found that 48% of the information that popular free chatbots gave in response to questions about the news contained errors.

As librarians, we know that if students are using AI chatbots to help with their academic research, they shouldn’t assume that the information they’re given is accurate.

Not all AI is the same. Here are four steps we recommend to question information AI generates, including by checking the original source and considering issues that aren’t always easy to spot.

1. Find the best fit

First, we think it makes sense to spend time choosing the right AI tool for the task at hand.

AI tools – be it ExLibris’ Primo Research Assistant, Research Solutions’ Scite, Anthropic’s Claude or OpenAI’s ChatGPT – are trained with various datasets and use different algorithms to generate results.
They should not be used interchangeably.

Some tools might be better or worse at answering specific kinds of questions, and the types of answers they generate can vary.

People might choose ChatGPT, for example, at the beginning of their research process, because it can provide overviews and summaries to help with understanding concepts.

In subsequent stages, they might choose a tool such as Primo Research Assistant or Scopus AI, which can help find and examine peer-reviewed research that has been vetted by academics.

Students conducting academic research could also see whether their library subscribes to an artificial intelligence tool like LeapSpace or Concensus, which draw their data from peer-reviewed, scholarly research.

Using these specific AI tools doesn’t eliminate the need to evaluate their answers, but it provides more certainty that the data has gone through a review process.

Tools such as Anthropic’s Claude, meanwhile, may also draw from academic research, but to what extent is unclear. According to Anthropic, Claude states that it is trained on a proprietary mix of publicly available information from online sources, public and private datasets, user data and synthetic data generated by other models.

Finally, it can be helpful to read librarians’ reviews about AI tools to help select the best fit. People can also take a simple online test such as ROBOT to help them consider which tool to use.

Checking the primary sources on the information that AI tools generate is one of the important steps researchers can take to ensure it is accurate. Andriy Onufriyenko/iStock/Getty Images

2. Ask more questions

Second, asking follow-up questions is an important part of evaluating the answers AI tools generate. Even if the answer an AI tool gives sounds good, it is useful to take time to learn more about it.

Checking one’s own emotions is also important, because AI chatbots have been known to flatter users by giving answers based on the user’s own biases, or what the user wants to hear, rather than just facts.

Ask the AI to tell you where it got the information, how confident it is that the answer is correct and what might be missing. Ask the AI why it believes the answer is correct, or ask it to explain how it came up with an answer.

It’s helpful to also make sure your prompt is phrased in a neutral way. For example, asking “What does psychological research say about social media and anxiety?” rather than “Tell me why social media causes anxiety” may produce different responses.

3. Check the source

Third, it is useful to trace any claims the AI chatbot makes to their original sources.

Students may be familiar with using the SIFT process to evaluate websites. This process asks people to stop, investigate the source, find better coverage and trace claims to their original source.

Start by asking the chatbot to cite its sources, if it did not do that already.

Then, go to each source and read enough to figure out whether the AI is using the information in the right context. Also, look into who authored the source and whether they have expertise in the topic. If the AI won’t cite the sources it used, or if the sources it cites don’t include the information it said they did, pull out any claims it is making and do further research.

Can you find other, credible sources that back up what the AI is saying, using Google or a library database? Can you figure out where the AI may have gotten the information and whether that source is credible?

If this seems like a lot of work, you could always go directly to library databases instead of using AI tools.

4. Not all issues are easy to spot

Fourth, there are other, less visible issues that can play out when using AI tools.

For example, it might not always be easy to tell whether an AI tool is missing important perspectives or is giving biased information.

Remember that you, not the AI tool, are the researcher. It might be easy to rely on an AI tool to generate a summary of important sources, but deciding for yourself what sources to use helps you learn about a topic more deeply.

Research shows that most AI tools focus on sources such as websites, internet forums and other online content that is freely available and written in English. Consider what expert knowledge might be missing from AI datasets and seek out those viewpoints when needed.

Last, remember that you need to cite the AI tool you used if you are quoting or paraphrasing text generated by it in your work, just as you would cite any other source.

Artificial intelligence is changing fast. As librarians, we think it is important to continually evaluate whether the tools you are using are still working for you.

These tips are a good place to start to help sharpen your evaluation skills.

Allison Faix, Librarian and Instruction Coordinator, Coastal Carolina University and Samantha Duncan, Instructional Design Librarian, Coastal Carolina University

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

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27% of Americans Use AI for Personal Advice, 20% for Stress Relief, and 19% for Mental Health Information

By Felix Richter, Statista

As AI has quickly become more capable and human-like in the way it responds to questions, many people are turning to chatbots as an accessible, non-judgmental way to discuss personal issues or mental health problems. Data suggests that Americans are using AI tools to navigate personal dilemmas and emotional challenges.

According to a recent Statista Consumer Insights survey, getting personal advice or guidance is by far the most popular AI use case related to personal concerns and mental well-being, cited by 27 percent of U.S. respondents. Meanwhile, one in five Americans reported turning to AI tools specifically to reduce stress or anxiety, and 19 percent use them to learn more about mental health.

Other common motivations include self-reflection and personal growth, talking through personal concerns, distraction and practicing mindfulness. 12 percent of respondents use chatbots to combat feelings of loneliness, a trend mirrored in the rise of AI companion apps specifically designed to mimic human connection. While 43 percent of respondents say they do not turn to AI for mental well-being or personal topics at all, those who do largely perceive a benefit: 70 percent of AI well-being users reported finding the tools either very or somewhat helpful.

Fact-Checked by Irfan Ahmad.

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• Arcadia University Professor Shares Insights for Staying Safe Online

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Arcadia University Professor Shares Insights for Staying Safe Online

By Tim Pierce, Arcadia University

October is Cybersecurity Awareness Month, calling attention to the need to protect ourselves against online threats.

Image: Julio Lopez - Unsplash

Dr. Vitaly Ford, associate professor of Computer Science, is an expert in the computing field, including online security, privacy, and machine learning.

With threats becoming more and more sophisticated each day, he has some tips and tricks for staying safe during Cybersecurity Awareness Month and beyond.

This Q&A has been slightly edited for clarity and length.

What are some of the biggest cybersecurity threats that ordinary people face today? What risks do you think people are still underestimating?

Scammers are still on the rise with fake emails, texts, and phone calls, and it’s only getting worse. Free tools like modlishka, evilginx3, and evilworker let them sit between you and a real login page, so they can steal your whole logged-in session and get around normal two-factor authentication (the extra code you type in).

Other common tricks include fake WiFi, downloads that infect your device just from visiting a site, and fake pop-ups on sites that get you to install malware masquerading as an “update.” There are even attacks that abuse a weakness in SS7, an old 1970s phone network protocol, or use SIM-swapping–taking over your phone number–to go after high-profile individuals and intercept their text messages and phone calls.

As the list of threats keeps growing, I recommend checking your data leaks through haveibeenpwned.com, haveibeenflocked.com, and att.pentester.com.

With artificial intelligence making it easier to create convincing emails, messages, websites, and even voices, how is AI changing the way people should think about cybersecurity?

With deepfakes, automated attacks, and convincing messages, artificial intelligence has escalated hacking capabilities worldwide. We can even call it vibe hacking at this point.

I recommend “Stop Trusting -> Always Verifying.” This should be the motto for any site interaction and message we receive. Is it a bank contacting you? Hang up and call back on a trusted line, go to their site yourself, or check in your bank app. Seeing a pop-up asking you to update your system? Cancel and check for updates separately yourself. Avoid blind downloads without verification of what you get.

If readers want to take immediate actions to become more cybersecurity aware, what would you recommend?

1. Freeze your credit with Equifax, Experian, and TransUnion – it’s free. Thawing it is easy and takes only a minute in case you need to apply for a loan or credit card. This will avoid many potential issues with identity theft.

2. Sign up for data breach notifications at haveibeenpwned.com (click on Notify button).

3. Use Cloudflare WARP (https://one.one.one.one). Think of it as a better version of “VPN” because it not only encrypts, but also blocks nasty stuff. If it doesn’t work on your device for some reason, try the personal/free version of Proton VPN. Never connect to free WiFi unless you have WARP or a VPN that you trust.

4. Antivirus is far from a panacea, but it will help catch at least the “common cold.” Avoid disabling it. But don’t install a random antivirus; do your research.

5. Use passkeys or physical keys (e.g., YubiKey), if you can, for your 2FA. The third-best option for 2FA is authenticator apps (e.g., Google Auth).

6. Always, always check what domain you are on.

7. Check the email sender and the original raw email (e.g., in Gmail it’s in the top-right corner -> “Show original” menu) and make sure that at the top you see all SPF, DKIM, DMARC as PASS. If not, question the email.

Image: DIW. CC BY

8. Learn about your privacy at operationprivacy.com and deflock.org.

Fact-Checked by Irfan Ahmad.

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Waze Police Alerts Cut Speeding From 64% to 12% on Australian 100 km/h Roads

By Julie Schomberg, University of the Sunshine Coast

Drivers alerted to police presence by others using a popular global app appear to be learning how to avoid speeding penalties – but not how to stay safe on roads.

An Australian-first study has found that more than 50 percent of drivers were speeding prior to a police enforcement alert appearing on the Waze navigation app – and this dropped to about 25 percent after the alert.

“Drivers weren’t slowing down because of the speed limit. They were slowing because their phone told them there would be police ahead,” said University of the Sunshine Coast Research Associate Dr Levi Anderson.

“In addition, the alerts usually appeared within a 14-minute window of UniSC researchers starting to take speed measurements, quickly warning drivers of what they understood to be police enforcement ahead.”

Image: Jack McCracken - Unsplash

Waze is a Google subsidiary with more than 150 million monthly active users worldwide. In Australia, a third of drivers reportedly use apps such as Waze to share observations of road and traffic conditions.

Dr Anderson said the effect was recorded across all 33 study sites in South East Queensland, covering diverse speed zones and environments.

UniSC researchers used a laser speed measurement device to record more than 9,000 vehicle speeds in various environments, including arterial roads, residential streets, school zones, commercial or industrial areas, and roadworks.

“This effect also grew with speed, with the greatest speed changes recorded on highways, especially at roadworks,” he said.

“In a 100kmh zone, for example, the proportion of speeding drivers fell from 64 percent to 12 percent after an enforcement alert was shared on the app.

“The smallest reaction, however, occurred in active school zones where 38 percent of drivers continued speeding above the 40kmh limit despite an app alert.”

Overall, the average speed difference across all zones between vehicles travelling before and after the alert was about 5kmh.

Dr Anderson’s paper ‘Waze to beat the system’ was published on October 5 in the Journal of Experimental Criminology.

“Despite the mass adoption of platforms like Waze, this is the first research to use direct observation of laser-measured vehicle speeds to find out if crowdsourced alerts actually influence driver behaviour,” he said.

“These results, along with other research, suggest that app alerts provide a short-term, localised benefit in reducing speeds and crash risks at particular sites for limited times, but are a long-term detriment to road safety as drivers become emboldened to speed if the app doesn’t warn them about police ahead.

“Drivers who did not slow may not have had the Waze app, or hoped their illegal speed was insufficient to trigger an infringement notice.”

Dr Anderson, a member of the MAIC/UniSC Road Safety Research Collaboration, said the practical implications of the study have already been shared with frontline police.

“In this study, the laser device was being used by researchers – not police – but the findings can certainly help inform how police conduct speed enforcement,” he said.

“This could include rapidly moving locations within that 14-minute time period between police setting up a checkpoint and an alert appearing on Waze.

“If longer, static police operations have more chance of being advertised on an app, police may opt for shorter, geographically dispersed deployments.”

Peter Flanders, a study contributor and UniSC adviser who was a Queensland Police officer for more than four decades, said speeding remained one of the most significant contributors to road trauma globally.

“In Queensland, it is a contributing factor in approximately one-third of fatal crashes, making it a persistent priority for road safety stakeholders and enforcement,” he said.

“Previous research has established that as vehicle speeds increase, both the likelihood and severity of crashes rise exponentially. The perceived probability of being caught exerts a stronger deterrent effect than the severity of the penalty.

“The ‘halo effect’ is where speed reductions occur not only at the site of enforcement, but also in the surrounding areas and for a time after that enforcement site.

“The extent of the halo, however, depends on drivers believing that enforcement is both present and unpredictable. Technology has altered these dynamics.

“This study could help inform strategic operational planning, to help police leverage app alerts to improve driver behaviour and road safety for everyone on the road.”

Fact-Checked by Irfan Ahmad.

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Daily doses of ‘digital ibuprofen’: how burnt‑out Chinese youth soothe their anxieties

By Hui-Ying Kerr, Nottingham Trent University

Fact-Checked by Irfan Ahmad.

In China, young people are encountering myriad challenges, from societal pressures around success to existing in a highly surveilled society, that have made them turn to the internet in search of a balm to soothe their pain. What has emerged is a host of content called digital ibuprofen – viewed as a way to numb the anxieties of modern living.

The most popular digital ibuprofen at the moment is the Japanese webcomic Chiikawa (2020), a portmanteau of the Japanese words small (chiisai) and cute (kawaii). Set within what has been called a “post-apocalyptic gig economy”, Chiikawa follows the daily lives, jobs (like weeding and monster hunting) and struggles of small, emotional creatures. The appeal of the sweet, strange world to a disaffected youth becomes apparent as darker themes emerge.

Image: Nguyen Hung - Pexels

These animals face challenges like sitting exams to get higher pay, doing menial labour to survive, and suffering burnout. Along the way they try to avoid scams and monsters that want to eat them. But amidst it all they manage to maintain positivity by cultivating friendships and enjoyment, especially by sharing delicious food.

Rather than the explicit cynicism of western cartoons such as The Simpsons and South Park, much of digital ibuprofen represents a distinctly East Asian approach to social satire that uses cuteness and surrealism to disguise its message. Chiikawa is part of a lineage that includes characters like Aggretsuko (2016) – a cute red panda who de-stresses from office life by singing death metal at karaoke – and Gudetama (2013) – an apathetic, depressed egg yolk.

Pay attention and you’ll see that these characters are trying to navigate many of the same issues that Chinese youth face. This is a group who spends their school years frantically studying for highly competitive high school final exams called gaokao.

They are then faced with entering a brutalising work environment where 996 has become a shorthand for the normalisation of working 9am till 9pm, six days a week. Such unrelenting demands of work have been widely documented as China emerged from its rapid development in the early 21st Century.

Burnout has become common and the Chinese dream seems to have receded in the face of economic downturn. In response, movements such as “lying flat” (tang ping), meaning doing the bare minimum, or becoming a full-time child – taking a career break to live at home and help parents – have emerged.

Then there’s the impact of the explosion of China’s digital landscape. Following huge infrastructural investment, China has become a technology powerhouse with widespread engagement with Chinese social media and innovations in goods and services online.

Yet this growth has come with other issues. There are more online scams and a constant awareness of government surveillance and control over public speech. These young people are living a world where online comments are monitored, deleted or worse. In some instances, a thoughtless post could lead to an invitation for “a cup of tea” (informal questioning) at the police station.

So digital ibuprofen becomes an apt analogy for the psychological desire to be numbed in the face of all these daily challenges – even if the internet simultaneously feels like a dangerous place.

The cuteness inherent in these soothing products has a significant history in East Asia, not only in sweet characters such as Sanrio’s Hello Kitty, but also in the late 1990s and early 2000s Japanese healing boom (iyashi).

In response to economic recession and the resulting loss of stability for many, new goods and services, such as cat cafes, massage and aromatherapy, became popular ways for workers to de-stress. Japanese media also promoted manga and anime that focused on peaceful environments and low-conflict daily life to comfort their audiences.

For instance, Aria (2001), a show about a young woman training as a gondola tour guide on a futuristic, water-covered Mars, focuses on the small, everyday joys of life.

Digital ibuprofen is just the latest evolution of this tendency to seek comfort from media that promise to heal and understand us in the face of modern pressures.

As the darker themes in Chiikawa attest, within this cuteness is what has become a uniquely East Asian type of protest – that of soft rebellion. This is where extreme passivity and retreat into childhood things is used to articulate a dissatisfaction with the demands of the adult world to grow up. There is a quiet resistance through non-engagement.

From 1960s Japanese university students openly reading manga comics in class to the childlike clothes of Lolita fashion subcultures, extreme cuteness has signalled a resistance to becoming mature, responsible producers in an economic system stacked against them.

Also, in a conservative society based on traditional Confucian principles of order and hierarchy, soft rebellion neatly circumvents restrictions and limitations without the need for open confrontation, which can invite real risks and consequences from an uncompromising state.

But, while digital ibuprofen may look like it merely numbs the pain rather than deals with the root cause, it also allows youth to explore their dissatisfaction safely, without inviting trouble. Although you have to earn a living, the message of Chiikawa is that goodness is present wherever you find it, and you don’t need a lot in life. There’s friends to share good times, to help each other through hard times. And at the end of the day there’s always cake.

Hui-Ying Kerr, Senior Lecturer in Fashion Communication and Promotion, Nottingham Trent University

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

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Is this the ‘mathocalypse’? Why OpenAI’s latest results dump has left mathematicians in shock

By Melissa Lee, Monash University

Image: Brecht Corbeel - Unsplash

Earlier this week, OpenAI published a trove of hundreds of mathematical results it claimed could “push the frontier of human knowledge”. Produced largely by an unreleased artificial intelligence (AI) model, the 722 papers relate to 372 open problems covering everything from algebra and geometry to theoretical computer science.

Several papers have since been retracted or amended, but the sheer scale and form of the release – variously described as a drop, a dump, a carpet bombing and a mathocalypse – have sent shockwaves through the mathematical community.

Some papers claim significant advances on high-profile, long-standing problems including the Riemann hypothesis and the Birch–Swinnerton-Dyer conjecture, each of which carries a bounty of US$1 million (A$1.4 million) if solved as one of the seven Millenium Prize Problems.

Why has OpenAI done this? And can we trust the claimed solutions are correct? Neither of these questions seem to have particularly obvious answers.

A strange way to enable progress

OpenAI says its motivation in releasing the huge trove of results is to “enable further progress in mathematics”. However, the manner of the release is not necessarily conducive to this goal.

A senior colleague of mine found one of his favourite problems among those solved and attempted to read the accompanying paper. He told me it was so unintelligible that, had he received it as an editor at a mathematics journal, “it would have gone straight into the bin”.

Even OpenAI’s own large language model ChatGPT (GPT-5.6 Sol, to be precise) was skeptical when I asked it, describing at least one of the high-profile results as “a serious hallucination … [which] should never be cited, submitted, or circulated as a proof without a complete expert audit”.

In other words, despite the groundbreaking nature of some of the results obtained, the way at least some of the accompanying papers are written is not comprehensible even to experts.

Changing the conversation

OpenAI also may be seeking to move the discussion on from its last big mathematical publication. In September, the company published a controversial claimed solution of a case of the Navier-Stokes problem, another $1,000,000 Millenium Problem.

Mathematicians Tristan Buckmaster and Levent Alpöge, who had been working on the problem themselves, alleged OpenAI had accessed their data and used their ideas, then devoted some US$15 million of computing power to find a solution. OpenAI has denied these allegations.

One other possible broader motivation for OpenAI is the hot topic of artificial general intelligence (AGI). This is a hypothetical type of AI that could surpass humans in basically all cognitive tasks.

Mathematics – especially pure, abstract mathematics – is a discipline that largely relies on often very complex arguments to prove the truth of mathematical statements. It is often seen as a subject that is very difficult for most people.

Hence, the ability of OpenAI’s models to do research-level mathematics may lend credence to the idea the models are approaching the grail of AGI. This idea could benefit OpenAI ahead of the company’s planned stock market launch. The company reportedly hopes to achieve a valuation of up to US$1.4 trillion – about 4% of the entire US gross domestic product.

How can we trust the mathematics is correct?

One of the key features of mathematics (pure mathematics especially) is its foundation in objective truth. Statements are either true or false, and there is almost never any ambiguity.

For centuries, we have agreed mathematical truth by consensus among mathematicians. In particular, new work is refereed by experts who check it is correct before it is published and becomes part of the “literature”.

This process can be very time consuming. Some longer, technical papers can take years to review.

So the eventual test of OpenAI’s results will be what the mathematical community makes of them after proper examination.

OpenAI has already retracted three of the papers due to an elementary error and amended several others due to mistakes that invalidated their results. This suggests, at the least, a lack of sufficient vetting before publication.

Machines checking machines

Over the past decade or more, there has been a push to “formalise” results with software systems such as Lean. These systems build up mathematics from basic principles or axioms, which means the software can check each step of logic in a result.

Until recently, formalisation efforts were largely focused towards theory and problems that had already been solved. One early example was the 2008 formalisation of the decades-old four colour theorem in graph theory, about the minimum number of colours required to colour a map.

Formalising a result takes a lot of knowledge and effort. However, AI systems are now being used for “autoformalisation” – automatically translating a result into a machine-checkable proof.

To encourage confidence in the correctness of its results, OpenAI claims to have formalised 300 of the main results at the time of writing.

However, not all formalisations are implemented correctly – and there is past evidence of problems with AI-generated formalisations, including that of the Navier-Stokes problem. It is currently unclear whether the mathematical community will accept OpenAI’s formalisations.

Excitement – and concern

Reactions to the OpenAI drop have been mixed, ranging from “obviously the most significant moment in mathematical history” to “mathematicians aren’t so thrilled at this giant pile of turd dumped at our doorstep”.

There is certainly great excitement about progress towards important mathematical problems in a variety of fields as experts attempt go over the results with a fine-toothed comb.

For many, this excitement is also mixed with uncertainty and concern: what does the volume and speed of these discoveries mean for the future of mathematics research, and how we are contributing to it? And who should carry the labour of trying to make sense of arguments that initially read as AI slop?

These papers, along with other AI-assisted developments, have forced the mathematical community to reflect on what we value. This is particularly important for students and early career researchers, who must confront the idea of their research being scooped by an ambitious AI user, or how their work will be valued.

And that conversation will continue long after the dust has settled on this latest tranche of discoveries.

Melissa Lee, Senior Lecturer, School of Mathematics, Monash University

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

Fact-Checked by Irfan Ahmad.

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44% of Americans Want AI Price Comparisons, and 37% Bought Products Based on AI Recommendations

By Katharina Buchholz, Statista. 

Fact-Checked by Irfan Ahmad.

Consumers want some real savings out of AI use while shopping: In a recent Statista Consumer Insights survey published in the Statista Consumer Reality Update 2026, 44 percent of Americans say they wanted AI to compare prices and find them the best value for money.

In this scenario, however, U.S. consumers have already made up their minds about the product they want to purchase. In terms of AI helping with these decisions, 41 percent said they were open to discovering new products to purchase via AI and 34 percent said they were even willing to pay more if AI made a good case for a product. 39 percent said that AI recommendations made a purchase more likely.

Overall, the survey showed that consumers were actually hesitant to let AI make decisions for them instead of just helping with them. 42 percent said they would not trust AI to make these decisions and the remaining share would only do so in certain categories, the largest being clothing and accessories at 27 percent saying they would AI carry out a purchase on its own.

Only 37 percent said they had made a purchase based on AI recommendations in the past year, with 16 percent having made one such purchase and 21 percent having made more than one.

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