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Student trust in AI coding tools grows briefly, then levels off with experience

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Student trust in AI coding tools grows briefly, then levels off with experience


Screenshot of the code students worked on in the study. Credit: University of California – San Diego

How much do undergraduate computer science students trust chatbots powered by large language models like GitHub Copilot and ChatGPT? And how should computer science educators modify their teaching based on these levels of trust?

These were the questions that a group of U.S. computer scientists set out to answer in a study that will be presented at the Koli Calling conference Nov. 11 to 16 in Finland. In the course of the study’s few weeks, researchers found that trust in generative AI tools increased in the short run for a majority of students.

But in the long run, students said they realized they needed to be competent programmers without the help of AI tools. This is because these tools often generate incorrect or would not help students with code comprehension tasks.

The study was motivated by the dramatic change in the skills required from undergraduate students since the advent of generative AI tools that can create code from scratch. The work is published on the arXiv preprint server.

“Computer science and programming is changing immensely,” said Gerald Soosairaj, one of the paper’s senior authors and an associate teaching professor in the Department of Computer Science and Engineering at the University of California San Diego.

Today, students are tempted to overly rely on chatbots to generate code and, as a result, might not learn the basics of programming, researchers said. These tools also might generate code that is incorrect or vulnerable to cybersecurity attacks. Conversely, students who refuse to use chatbots miss out on the opportunity to program faster and be more productive.

But once they graduate, computer science students will most likely use generative AI tools in their day-to-day, and need to be able to do so effectively. This means they will still need to have a solid understanding of the fundamentals of computing and how programs work, so they can evaluate the AI-generated code they will be working with, researchers said.

“We found that student trust, on average, increased as they used GitHub Copilot throughout the study. But after completing the second part of the study–a more elaborate project–students felt that using Copilot to its full extent requires a competent programmer that can complete some tasks manually,” said Soosairaj.

The study surveyed 71 junior and senior computer science students, half of whom had never used GitHub Copilot. After an 80-minute class where researchers explained how GitHub Copilot works and had students use the , half of the students said their trust in the tool had increased, while about 17% said it had decreased. Students then took part in a 10-day-long project where they worked on a large open-source codebase using GitHub Copilot throughout the project to add a small new functionality to the codebase.

At the end of the project, about 39% of students said their trust in Copilot had increased. But about 37% said their trust in Copilot had decreased somewhat while about 24% said it had not changed.

The results of this study have important implications for how computer science educators should approach the introduction of AI assistants in introductory and advanced courses. Researchers make a series of recommendations for computer science educators in an undergraduate setting.

  • To help students calibrate their trust and expectations of AI assistants, computer science educators should provide opportunities for students to use AI programming assistants for tasks with a range of difficulty, including tasks within large codebases.
  • To help students determine how much they can AI assistants’ output, computer science educators should ensure that students can still comprehend, modify, debug, and test code in large codebases without AI assistants.
  • Computer science educators should ensure that students are aware of how AI assistants generate output via processing so that students understand the AI assistants’ expected behavior.
  • Computer science educators should explicitly inform and demonstrate key features of AI assistants that are useful for contributing to a large code base, such as adding files as context while using the ‘explain code’ feature and using keywords such as “/explain,” “/fix,” and “/docs” in GitHub Copilot.

“CS educators should be mindful that how we present and discuss AI assistants can impact how students perceive such assistants,” the researchers write.

Researchers plan to repeat their experiment and survey with a larger pool of 200 students this winter quarter.

More information:
Anshul Shah et al, Evolution of Programmers’ Trust in Generative AI Programming Assistants, arXiv (2025). DOI: 10.48550/arxiv.2509.13253

Conference: www.kolicalling.fi/

Journal information:
arXiv


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Just in Time for Spring, Don’t Miss These Electric Scooter Deals

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Just in Time for Spring, Don’t Miss These Electric Scooter Deals


The snow is melting, the days are getting longer, and I can almost smell the springtime ahead. Soon, we’ll be cruising around town on ebikes and electric scooters instead of burning fossil fuels. For now, the weather hasn’t quite caught up, which is great for markdowns. Many of the best electric scooters are still seeing significant discounts. If you’ve been thinking about buying one, now’s the best time: prices are low, and sunny commuting days are just ahead.

Gear editor Julian Chokkattu has spent five years testing more than 45 electric scooters. These are his top picks that are also on sale right now.

Apollo Go for $849 ($450 Off)

Photograph: Julian Chokkattu

This is Gear editor Julian Chokkattu’s favorite scooter. The riding experience is powerful and smooth, thanks to its dual 350-watt motors and solid front and rear suspensions. The speed maxes out at 28 miles per hour (mph), which doesn’t make it the fastest scooter on the market, but it has a good range. (Chokkattu is a very tall man and was able to travel 15 miles on a single charge at 15 mph.) Other Apollo features he appreciates: turn signals, a dot display, a bell, along with a headlight and an LED strip for extra visibility.

Apollo Phantom 2.0 for $2099 ($900 Off)

  • Photograph: Julian Chokkattu

  • Photograph: Julian Chokkattu

  • Photograph: Julian Chokkattu

The Apollo Phantom 2.0 maxes out at 44 mph, with plenty of power from its dual 1,750-watt motors. It’s a gorgeous scooter, designed with 11-inch self-healing tubeless tires and a dual-spring suspension system for a smooth riding experience. But with great power comes great weight. At 102 pounds, the Phantom 2.0 is the heaviest electric scooter Chokkattu has tested, so I would only recommend this purchase if you don’t live in a walkup and/or have a garage.

More Discounted Electric Scooters

Segway

Max G3

This is the best commuter scooter, with more power and range than the Apollo Go and a fast 3.5-hour recharge time.

Segway

Ninebot F3 Electric Scooter

The Segway F3 is designed with turn signals, a bell, a bright display, and a feature-rich app experience.

Niu KQi 300X

This is the best all-terrain scooter, with reliable suspension, dual disc brakes, and thick 10.5-inch tubeless tires.

Segway

E2 Pro

This is the best budget scooter, designed with a decent 350-watt motor, a max speed of 15 mph, a front drum brake, and a rear electronic brake.



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What’s an E-Bike? California Wants You to Know

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What’s an E-Bike? California Wants You to Know


A few months ago, a family came into Pasadena Cyclery in Pasadena, California, for a repair on what they thought was their teenager’s e-bike. “I can’t fix that here,’ Daniel Purnell, a store manager and technician, remembers telling them. “That’s a motorcycle.” The mother got upset. She didn’t realize that what she thought was an e-bike could go much faster, perhaps up to 55 miles per hour.

“There’s definitely an education problem,” Purnell says. In California, bike advocates are pushing a new bill designed to clear up that confusion around what counts as an electric bicycle—and what doesn’t.

It’s a tricky balance. On one hand, backers want to allow riders access to new, faster, and more affordable non-car transportation options, ones that don’t require licenses and are emission-free. On the other hand, people, and especially kids, seem to be getting hurt. E-bike-related injuries jumped more than 1,020 percent nationwide between 2020 and 2024, according to hospital data, though it’s not clear if the stats-keepers can routinely distinguish between e-bikes and their faster, “e-moto” cousins. (Moped and powered-assisted cycle injuries jumped 67 percent in that same period.)

“We’re overdue to have better e-bike regulation,” says California state senator Catherine Blakespear, a Democrat who sponsored the bill and represents parts of North County in San Diego. “This has been an ongoing and growing issue for years.”

Senate Bill 1167 would make it illegal for retailers to label higher-powered, electric-powered vehicles as e-bikes. It would clarify that e-bikes have fully operative pedals and electric motors that don’t exceed 750 watts, enough to hit top speeds between 20 and 28 mph.

“We’re not against these devices,” says Kendra Ramsey, the executive director of the California Bicycle Coalition, which represents riders and is promoting the legislation. “People think they’re e-bikes and they’re not really e-bikes.”

Bill backers say they hope the fix, if it passes, makes a difference, especially for teenagers, who love the freedom that electric motors give them but can get into trouble if something goes wrong at higher speeds. Kids 17 and younger accounted for 20 percent of US e-bike injuries from 2020 to 2024, about in line with the share of the total population. But headlines—and the laws that follow them—have focused on teen injuries and even deaths.

There are no national laws governing e-bike riding. But bike backers spent years moving between states to pass laws that put e-bikes into three classes: Class 1, which have pedal-assist that only works when they’re actually pedaled, and goes up to 20 mph; Class 2, which have throttles that work without pedaling but still only reach 20 mph; and Class 3, which use pedal-assist to move up to 28 mph. Plenty of states and cities restrict the most powerful Class 3 bikes to people older than 16. (In a complicated twist, some e-bikes have different “modes,” allowing riders to toggle between Class 2 and Class 3.)

Last year, researchers visited 19 San Francisco Bay Area middle and high schools and found that 88 percent of the electric two-wheeled devices parked there were so high-powered and high-speed that they didn’t comply with the three-class system at all.

E-bikes have clearly struck a chord with state policymakers: At least 10 bills introduced this year deal with e-bikes, according to Ramsey.

Some bike advocates believe injuries have less to do with e-bikes than “e-motos,” a category that’s less likely to appear in retail stores or the sort of social media ads attracting teens to the tech. These have more powerful motors and can travel in excess of 30 mph. Vehicles, like the Surron Ultra Bee, which can hit top speeds of 55 mph, or Tuttio ICT, which can hit 50, are often marketed by retailers as “electric bikes.” Because so many sales happen online, it can be hard for people, and especially parents, to know what they’re getting into.



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OpenAI Fires an Employee for Prediction Market Insider Trading

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OpenAI Fires an Employee for Prediction Market Insider Trading


OpenAI has fired an employee following an investigation into their activity on prediction market platforms including Polymarket, WIRED has learned.

OpenAI CEO of Applications, Fidji Simo, disclosed the termination in an internal message to employees earlier this year. The employee, she said, “used confidential OpenAI information in connection with external prediction markets (e.g. Polymarket).”

“Our policies prohibit employees from using confidential OpenAI information for personal gain, including in prediction markets,” says spokesperson Kayla Wood. OpenAI has not revealed the name of the employee or the specifics of their trades.

Evidence suggests that this was not an isolated event. Polymarket runs on the Polygon blockchain network, so its trading ledger is pseudonymous but traceable. According to an analysis by the financial data platform Unusual Whales, there have been clusters of activities, which the service flagged as suspicious, around OpenAI-themed events since March 2023.

Unusual Whales flagged 77 positions in 60 wallet addresses as suspected insider trades, looking at the age of the account, trading history, and significance of investment, among other factors. Suspicious trades hinged on the release dates of products like Sora, GPT-5, and the ChatGPT Browser, as well as CEO Sam Altman’s employment status. In November 2023, two days after Altman was dramatically ousted from the company, a new wallet placed a significant bet that he would return, netting over $16,000 in profits. The account never placed another bet.

The behavior fits into patterns typical of insider trades. “The tell is the clustering. In the 40 hours before OpenAI launched its browser, 13 brand-new wallets with zero trading history appeared on the site for the first time to collectively bet $309,486 on the right outcome,” says Unusual Whales CEO Matt Saincome. “When you see that many fresh wallets making the same bet at the same time, it raises a real question about whether the secret is getting out.”

Prediction markets have exploded in popularity in recent years. These platforms allow customers to buy “event contracts” on the outcomes of future events ranging from the winner of the Super Bowl to the daily price of Bitcoin to whether the United States will go to war with Iran. There are a wide array of markets tied to events in the technology sector; you can trade on what Nvidia’s quarterly earnings will be, or when Tesla will launch a new car, or which AI companies will IPO in 2026.

As the platforms have grown, so have concerns that they allow traders to profit from insider knowledge. “This prediction market world makes the Wild West look tame in comparison,” says Jeff Edelstein, a senior analyst at the betting news site InGame. “If there’s a market that exists where the answer is known, somebody’s going to trade on it.”

Earlier this week, Kalshi announced that it had reported several suspicious insider trading cases to the Commodity Futures Trading Commission, the government agency overseeing these markets. In one instance, an employee of the popular YouTuber Mr. Beast was suspended for two years and fined $20,000 for making trades related to the streamer’s activities; in another, the far-right political candidate Kyle Langford was banned from the platform for making a trade on his own campaign. The company also announced a number of initiatives to prevent insider trading and market manipulation.

While Kalshi has heavily promoted its crackdown on insider trading, Polymarket has stayed silent on the matter. The company did not return requests for comments.

In the past, major trades on technology-themed markets have sparked speculation that there are Big Tech employees profiting by using their insider knowledge to gain an edge. One notorious example is the so-called “Google whale,” a pseudonymous account on Polymarket that made over $1 million trading on Google-related events, including a market on who the most-searched person of the year would be in 2025. (It was the singer D4vd, who is best known for his connection to an ongoing murder investigation after a young fan’s remains were found in a vehicle registered to him.)



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