Artificial Intelligence
Researchers Find Little Evidence of Cheating with Online, Unsupervised Exams
Students work on laptops above “Gene Pool,” a tile mosaic by Andrew Leicester inside the Molecular Biology Building at Iowa State University.
« Researchers Find Little Evidence of Cheating with Online, Unsupervised Exams
Newswise — AMES, IA — When Iowa State University switched from in-person to remote learning halfway through the spring semester of 2020, psychology professor Jason Chan was worried. Would unsupervised, online exams unleash rampant cheating?
His initial reaction flipped to surprise as test results rolled in. Individual student scores were slightly higher but consistent with their results from in-person, proctored exams. Those receiving B’s before the COVID-19 lockdown were still pulling in B’s when the tests were online and unsupervised. This pattern held true for students up and down the grading scale.
“The fact that the student rankings stayed mostly the same regardless of whether they were taking in-person or online exams indicated that cheating was either not prevalent or that it was ineffective at significantly boosting scores,” says Chan.
To know if this was happening at a broader level, Chan and Dahwi Ahn, a Ph.D. candidate in psychology, analyzed test score data from nearly 2,000 students across 18 classes during the spring 2020 semester. Their sample ranged from large, lecture-style courses with high enrollment, like introduction to statistics, to advanced courses in engineering and veterinary medicine.
Across different academic disciplines, class sizes, course levels and test styles (i.e., predominantly multiple choice or short answer), the researchers found the same results. Unsupervised, online exams produced scores very similar to in-person, proctored exams, indicating they can provide a valid and reliable assessment of student learning.
The research findings were recently published in Proceedings of the National Academy of Sciences.
“Before conducting this research, I had doubts about online and unproctored exams, and I was quite hesitant to use them if there was an option to have them in-person. But after seeing the data, I feel more confident and hope other instructors will, as well,” says Ahn.
Both researchers say they’ve continued to give exams online, even for in-person classes. Chan says this format provides more flexibility for students who have part-time jobs or travel for sports and extra-curriculars. It also expands options for teaching remote classes. Ahn led her first online course over the summer.
Why might cheating have had a minimal effect on test scores?
The researchers say students more likely to cheat might be underperforming in the class and anxious about failing. Perhaps they’ve skipped lectures, fallen behind with studying or feel uncomfortable asking for help. Even with the option of searching Google during an unmonitored exam, students may struggle to find the correct answer if they don’t understand the content. In their paper, the researchers point to evidence from previous studies comparing test scores from open-book and close-book exams.
Another factor that may deter cheating is academic integrity or a sense of fairness, something many students value, says Chan. Those who have studied hard and take pride in their grades may be more inclined to protect their exam answers from students they view as freeloaders.
Still, the researchers say instructors should be aware of potential weak spots with unsupervised, online exams. For example, some platforms have the option of showing students the correct answer immediately after they select a multiple-choice option. This makes it much easier for students to share answers in a group text.
To counter this and other forms of cheating, instructors can:
- Wait to release exam answers until the test window closes.
- Use larger, randomized question banks.
- Add more options in multiple-choice questions and making the right choice less obvious.
- Adjust grade cutoffs.
COVID-19 and ChatGPT
Chan and Ahn say the spring 2020 semester provided a unique opportunity to research the validity of online exams for student evaluations. However, there were some limitations. For example, it wasn’t clear what role stress and other COVID-19-related impacts may have played on students, faculty and teaching assistants. Perhaps instructors were more lenient with grading or gave longer windows of time to complete exams.
The researchers said another limitation was not knowing if the 18 classes in the sample normally get easier or harder as the semester progresses. In an ideal experiment, half of the students would have taken online exams for the first half of the semester and in-person exams for the second half.
They attempted to account for these two concerns by looking at older test score data from a subset of the 18 classes during semesters when they were fully in-person. The researchers found the distribution of grades in each class was consistent with the spring 2020 semester and concluded that the materials covered in the first and second halves of the semester did not differ in their difficulty.
At the time of data collection for this study, ChatGPT wasn’t available to students. But the researchers acknowledge AI writing tools are a gamechanger in education and could make it much harder for instructors to evaluate their students. Understanding how instructors should approach online exams with the advent of ChatGPT is something Ahn intends to research.
The study was supported by a National Science Foundation Science of Learning and Augmented Intelligence Grant.
Journal Link: Proceedings of the National Academy of Sciences
Source: Iowa State University
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Tech
How close are quantum computers to being really useful? Podcast
Quantum computers could revolutionize science by solving complex problems. However, scaling and error correction remain significant challenges before achieving practical applications.
Quantum computers have the potential to solve big scientific problems that are beyond the reach of today’s most powerful supercomputers, such as discovering new antibiotics or developing new materials.
But to achieve these breakthroughs, quantum computers will need to perform better than today’s best classical computers at solving real-world problems. And they’re not quite there yet. So what is still holding quantum computing back from becoming useful?
In this episode of The Conversation Weekly podcast, we speak to quantum computing expert Daniel Lidar at the University of Southern California in the US about what problems scientists are still wrestling with when it comes to scaling up quantum computing, and how close they are to overcoming them.
Quantum computers harness the power of quantum mechanics, the laws that govern subatomic particles. Instead of the classical bits of information used by microchips inside traditional computers, which are either a 0 or a 1, the chips in quantum computers use qubits, which can be both 0 and 1 at the same time or anywhere in between. Daniel Lidar explains:
“Put a lot of these qubits together and all of a sudden you have a computer that can simultaneously represent many, many different possibilities … and that is the starting point for the speed up that we can get from quantum computing.”
Faulty qubits
One of the biggest problems scientist face is how to scale up quantum computing power. Qubits are notoriously prone to errors – which means that they can quickly revert to being either a 0 or a 1, and so lose their advantage over classical computers.
Scientists have focused on trying to solve these errors through the concept of redundancy – linking strings of physical qubits together into what’s called a “logical qubit” to try and maximise the number of steps in a computation. And, little by little, they’re getting there.
In December 2024, Google announced that its new quantum chip, Willow, had demonstrated what’s called “beyond breakeven”, when its logical qubits worked better than the constituent parts and even kept on improving as it scaled up.
Lidar says right now the development of this technology is happening very fast:
“For quantum computing to scale and to take off is going to still take some real science breakthroughs, some real engineering breakthroughs, and probably overcoming some yet unforeseen surprises before we get to the point of true quantum utility. With that caution in mind, I think it’s still very fair to say that we are going to see truly functional, practical quantum computers kicking into gear, helping us solve real-life problems, within the next decade or so.”
Listen to Lidar explain more about how quantum computers and quantum error correction works on The Conversation Weekly podcast.
This episode of The Conversation Weekly was written and produced by Gemma Ware with assistance from Katie Flood and Mend Mariwany. Sound design was by Michelle Macklem, and theme music by Neeta Sarl.
Clips in this episode from Google Quantum AI and 10 Hours Channel.
You can find us on Instagram at theconversationdotcom or via e-mail. You can also subscribe to The Conversation’s free daily e-mail here.
Listen to The Conversation Weekly via any of the apps listed above, download it directly via our RSS feed or find out how else to listen here.
Gemma Ware, Host, The Conversation Weekly Podcast, The Conversation
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Leo Porter, University of California, San Diego and Daniel Zingaro, University of Toronto
What do you think there are more of: professional computer programmers or computer users who do a little programming?
It’s the second group. There are millions of so-called end-user programmers. They’re not going into a career as a professional programmer or computer scientist. They’re going into business, teaching, law, or any number of professions – and they just need a little programming to be more efficient. The days of programmers being confined to software development companies are long gone.
If you’ve written formulas in Excel, filtered your email based on rules, modded a game, written a script in Photoshop, used R to analyze some data, or automated a repetitive work process, you’re an end-user programmer.
As educators who teach programming, we want to help students in fields other than computer science achieve their goals. But learning how to program well enough to write finished programs can be hard to accomplish in a single course because there is so much to learn about the programming language itself. Artificial intelligence can help.
Lost in the weeds
Learning the syntax of a programming language – for example, where to place colons and where indentation is required – takes a lot of time for many students. Spending time at the level of syntax is a waste for students who simply want to use coding to help solve problems rather than learn the skill of programming.
As a result, we feel our existing classes haven’t served these students well. Indeed, many students end up barely able to write small functions – short, discrete pieces of code – let alone write a full program that can help make their lives better.
Tools built on large language models such as GitHub Copilot may allow us to change these outcomes. These tools have already changed how professionals program, and we believe we can use them to help future end-user programmers write software that is meaningful to them.
These AIs almost always write syntactically correct code and can often write small functions based on prompts in plain English. Because students can use these tools to handle some of the lower-level details of programming, it frees them to focus on bigger-picture questions that are at the heart of writing software programs. Numerous universities now offer programming courses that use Copilot.
At the University of California, San Diego, we’ve created an introductory programming course primarily for those who are not computer science students that incorporates Copilot. In this course, students learn how to program with Copilot as their AI assistant, following the curriculum from our book. In our course, students learn high-level skills such as decomposing large tasks into smaller tasks, testing code to ensure its correctness, and reading and fixing buggy code.
Freed to solve problems
In this course, we’ve been giving students large, open-ended projects and couldn’t be happier with what they have created.
For example, in a project where students had to find and analyze online datasets, we had a neuroscience major create a data visualization tool that illustrated how age and other factors affected stroke risk. Or, for example, in another project, students were able to integrate their personal art into a collage, after applying filters that they had created using the programming language Python. These projects were well beyond the scope of what we could ask students to do before the advent of large language model AIs.
Given the rhetoric about how AI is ruining education by writing papers for students and doing their homework, you might be surprised to hear educators like us talking about its benefits. AI, like any other tool people have created, can be helpful in some circumstances and unhelpful in others.
In our introductory programming course with a majority of students who are not computer science majors, we see firsthand how AI can empower students in specific ways – and promises to expand the ranks of end-user programmers.
Leo Porter, Teaching Professor of Computer Science and Engineering, University of California, San Diego and Daniel Zingaro, Associate Professor of Mathematical and Computational Sciences, University of Toronto
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Tech
From shrimp Jesus to fake self-portraits, AI-generated images have become the latest form of social media spam
Renee DiResta, Stanford University; Abhiram Reddy, Georgetown University, and Josh A. Goldstein, Georgetown University
Suppose you’ve spent time on Facebook over the past six months. In that case, you may have noticed photorealistic images that are too good to be true: children holding paintings that look like the work of professional artists, or majestic log cabin interiors that are the stuff of Airbnb dreams.
Others, such as renderings of Jesus made out of crustaceans, are just bizarre.
Like the AI image of the pope in a puffer jacket that went viral in May 2023, these AI-generated images are increasingly prevalent – and popular – on social media platforms. Even as many of them border on the surreal, they’re often used to bait engagement from ordinary users.
Our team of researchers from the Stanford Internet Observatory and Georgetown University’s Center for Security and Emerging Technology investigated over 100 Facebook pages that posted high volumes of AI-generated content. We published the results in March 2024 as a preprint paper, meaning the findings have not yet gone through peer review.
We explored patterns of images, unearthed evidence of coordination between some of the pages, and tried to discern the likely goals of the posters.
Page operators seemed to be posting pictures of AI-generated babies, kitchens or birthday cakes for a range of reasons.
There were content creators innocuously looking to grow their followings with synthetic content; scammers using pages stolen from small businesses to advertise products that don’t seem to exist; and spammers sharing AI-generated images of animals while referring users to websites filled with advertisements, which allow the owners to collect ad revenue without creating high-quality content.
Our findings suggest that these AI-generated images draw in users – and Facebook’s recommendation algorithm may be organically promoting these posts.
Generative AI meets scams and spam
Internet spammers and scammers are nothing new.
For more than two decades, they’ve used unsolicited bulk email to promote pyramid schemes. They’ve targeted senior citizens while posing as Medicare representatives or computer technicians.
On social media, profiteers have used clickbait articles to drive users to ad-laden websites. Recall the 2016 U.S. presidential election, when Macedonian teenagers shared sensational political memes on Facebook and collected advertising revenue after users visited the URLs they posted. The teens didn’t care who won the election. They just wanted to make a buck.
In the early 2010s, spammers captured people’s attention with ads promising that anyone could lose belly fat or learn a new language with “one weird trick.”
AI-generated content has become another “weird trick.”
It’s visually appealing and cheap to produce, allowing scammers and spammers to generate high volumes of engaging posts. Some of the pages we observed uploaded dozens of unique images per day. In doing so, they followed Meta’s own advice for page creators. Frequent posting, the company suggests, helps creators get the kind of algorithmic pickup that leads their content to appear in the “Feed,” formerly known as the “News Feed.”
Much of the content is still, in a sense, clickbait: Shrimp Jesus makes people pause to gawk and inspires shares purely because it is so bizarre.
Many users react by liking the post or leaving a comment. This signals to the algorithmic curators that perhaps the content should be pushed into the feeds of even more people.
Some of the more established spammers we observed, likely recognizing this, improved their engagement by pivoting from posting URLs to posting AI-generated images. They would then comment on the post of the AI-generated images with the URLs of the ad-laden content farms they wanted users to click.
But more ordinary creators capitalized on the engagement of AI-generated images, too, without obviously violating platform policies.
Rate ‘my’ work!
When we looked up the posts’ captions on CrowdTangle – a social media monitoring platform owned by Meta and set to sunset in August – we found that they were “copypasta” captions, which means that they were repeated across posts.
Some of the copypasta captions baited interaction by directly asking users to, for instance, rate a “painting” by a first-time artist – even when the image was generated by AI – or to wish an elderly person a happy birthday. Facebook users often replied to AI-generated images with comments of encouragement and congratulations
Algorithms push AI-generated content
Our investigation noticeably altered our own Facebook feeds: Within days of visiting the pages – and without commenting on, liking or following any of the material – Facebook’s algorithm recommended reams of other AI-generated content.
Interestingly, the fact that we had viewed clusters of, for example, AI-generated miniature cow pages didn’t lead to a short-term increase in recommendations for pages focused on actual miniature cows, normal-sized cows or other farm animals. Rather, the algorithm recommended pages on a range of topics and themes, but with one thing in common: They contained AI-generated images.
In 2022, the technology website Verge detailed an internal Facebook memo about proposed changes to the company’s algorithm.
The algorithm, according to the memo, would become a “discovery-engine,” allowing users to come into contact with posts from individuals and pages they didn’t explicitly seek out, akin to TikTok’s “For You” page.
We analyzed Facebook’s own “Widely Viewed Content Reports,” which lists the most popular content, domains, links, pages and posts on the platform per quarter.
It showed that the proportion of content that users saw from pages and people they don’t follow steadily increased between 2021 and 2023. Changes to the algorithm have allowed more room for AI-generated content to be organically recommended without prior engagement – perhaps explaining our experiences and those of other users.
‘This post was brought to you by AI’
Since Meta currently does not flag AI-generated content by default, we sometimes observed users warning others about scams or spam AI content with infographics.
Meta, however, seems to be aware of potential issues if AI-generated content blends into the information environment without notice. The company has released several announcements about how it plans to deal with AI-generated content.
In May 2024, Facebook will begin applying a “Made with AI” label to content it can reliably detect as synthetic.
But the devil is in the details. How accurate will the detection models be? What AI-generated content will slip through? What content will be inappropriately flagged? And what will the public make of such labels?
While our work focused on Facebook spam and scams, there are broader implications.
Reporters have written about AI-generated videos targeting kids on YouTube and influencers on TikTok who use generative AI to turn a profit.
Social media platforms will have to reckon with how to treat AI-generated content; it’s certainly possible that user engagement will wane if online worlds become filled with artificially generated posts, images and videos.
Shrimp Jesus may be an obvious fake. But the challenge of assessing what’s real is only heating up.
Renee DiResta, Research Manager of the Stanford Internet Observatory, Stanford University; Abhiram Reddy, Research Assistant at the Center for Security and Emerging Technology, Georgetown University, and Josh A. Goldstein, Research Fellow at the Center for Security and Emerging Technology, Georgetown University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
The science section of our news blog STM Daily News provides readers with captivating and up-to-date information on the latest scientific discoveries, breakthroughs, and innovations across various fields. We offer engaging and accessible content, ensuring that readers with different levels of scientific knowledge can stay informed. Whether it’s exploring advancements in medicine, astronomy, technology, or environmental sciences, our science section strives to shed light on the intriguing world of scientific exploration and its profound impact on our daily lives. From thought-provoking articles to informative interviews with experts in the field, STM Daily News Science offers a harmonious blend of factual reporting, analysis, and exploration, making it a go-to source for science enthusiasts and curious minds alike. https://stmdailynews.com/category/science/
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