This paper explores the synergy between human cognition and Large Language Models (LLMs), highlighting how generative AI can drive personalized learning at scale. We discuss parallels between LLMs and human cognition, emphasizing both the promise and new perspectives on integrating AI systems into education. After examining challenges in aligning technology with pedagogy, we review AutoTutor-one of the earliest Intelligent Tutoring Systems (ITS)-and detail its successes, limitations, and unfulfilled aspirations. We then introduce the Socratic Playground, a next-generation ITS that uses advanced transformer-based models to overcome AutoTutor's constraints and provide personalized, adaptive tutoring. To illustrate its evolving capabilities, we present a JSON-based tutoring prompt that systematically guides learner reflection while tracking misconceptions. Throughout, we underscore the importance of placing pedagogy at the forefront, ensuring that technology's power is harnessed to enhance teaching and learning rather than overshadow it.

It might look like nothing more than a camera on a stick, but this AI traffic cop could help to crack down on bad drivers. Today, Safer Roads Humber will deploy an AI-powered mobile camera to catch drivers on their phones and not wearing seatbelts. The camera, operated by Australian road safety company Acusensus, will be on the roads of East Yorkshire and Northern Lincolnshire for a week. This is the second time the AI camera has been deployed in the area as part of a UK-wide trial conducted by National Highways. Ian Robertson, from the Safer Roads Humber partnership, says: 'This state-of-the-art equipment increases our enforcement capability.'

Connor Leahy remembers the time he first realized AI was going to kill us all. It was 2019, and OpenAI's GPT-2 had just come out. Leahy downloaded the nascent large language model to his laptop, and took it along to a hackathon at the Technical University of Munich, where he was studying. In a tiny, cramped room, sitting on a couch surrounded by four friends, he booted up the AI system. Even though it could barely string coherent sentences together, Leahy identified in GPT-2 something that had been missing from every other AI model up until that point.

Perched on the edge of a curb, squinting down the length of Market Street in the US city of San Francisco, I find myself twitchily tapping my phone, only to wince as the time ticks away. And up the Market Street rail line rumbles my deliverance: a 1928 wood-panelled tram in green-and-white trim. Oh, the irony: I'm headed to test out one of the city's newest transportation options while creaking down the road on one of its oldest. San Francisco has long been a hub for transportation innovation. It was here that the first cable car system was put into use.

A van with technology that can automatically detect drivers holding a mobile phone at the wheel or not wearing a seatbelt is being trialled in the UK for the first time. National Highways are working with Warwickshire police to try out the "sensor test vehicle" on motorways and major A roads, and drivers who are caught may be prosecuted. The initial three-month trial will determine how the technology can be further deployed in future. Insp Jem Mountford, of Warwickshire police, said: "We are really excited to see the impact that this new technology has on the behaviour of drivers in Warwickshire. "During the trial the most serious breaches may be prosecuted, with others receiving warning letters, giving us the opportunity to explain how they have been caught and asking them to change their behaviour.

Many years ago, one couldn't be blamed for a car accident at night. After all, there were no headlights or streetlights to see what was coming, no traffic lights at intersections and no weather apps to warn us of bad road conditions. The impact of a traffic accident was expected to be bad because there were no seatbelts, airbags or apps to call for help. As advancements in vehicle and traffic safety have evolved, we are more accountable for anticipating what could go wrong and for using available tools to prevent it. We wear seatbelts, use airbags and rely on signals to warn us about what's ahead.

A new spy-in-the-sky camera which identified 15,000 cases of drivers using mobile phones could also catch motorists eating, drinking, or not wearing a seatbelt, its makers say. Smart cameras linked to a new, automated system using artificial intelligence (AI) are being trialled on an undisclosed motorway - ahead of a blanket ban on holding a mobile device while driving which comes into force in early 2022. The cameras instantly analyse high-definition photos taken through the windscreen of passing cars, and Jenoptik, the enforcement technology firm testing the cameras in the UK, believes they will be crucial in providing evidence to prosecute offenders. The pilot scheme has been running since spring and it is hoped a wider rollout across the country will be possible next year. But Acusensus, the Australian firm who designed the cameras, admits that they can be used to catch motorists doing anything from eating, drinking, smoking, adjusting the radio or using navigation devices in a holder.

Amazon is well-known for its technological Taylorism: using digital sensors to monitor and control the activity of its workers in the name of efficiency. But after installing machine learning-powered surveillance cameras in its delivery vans earlier this year, the company is now telling employees: agree to be surveilled by AI or lose your job. As first reported by Vice, Amazon delivery drivers in the US now have to sign "biometric consent" forms to continue working for the retailing giant. Exactly what information is being collected seems to vary based on what surveillance equipment has been installed in any given van, but Amazon's privacy policy (embedded below) covers a wide range of data. The data that drivers must consent to be collected includes photographs used to verify their identity; vehicle location and movements (including "miles driven, speed, acceleration, braking, turns, following distance"); "potential traffic violations" (like speeding, failure to stop at stop signs, and undone seatbelts); and "potentially risky driver behavior, such as distracted driving or drowsy driving."

Two men died near Houston, Texas, on Saturday while riding in a 2019 Tesla Model S that, according to local authorities, was speeding into a turn and ended up going off the road and crashing into a tree. It took first responders four hours and more than 30,000 gallons of water to put out the resulting fire, which kept reigniting; when damaged, the lithium ion batteries in electric cars can cause fires that are very difficult to extinguish because of how they store energy. Authorities reportedly attempted to ask Tesla for advice on how to put out the fire, but it's unclear whether they ended up getting any help. Besides the fire, there was something especially disturbing about the crash: No one was in the driver's seat. One of the men was in the passenger seat and the other in the rear.

Vic, who asked the Thomson Reuters Foundation to use only his first name "for fear of retaliation," this month quit his job delivering packages for the tech giant. He started work in 2019 and saw Amazon's policies change to include more active means of surveillance. First there was an app tracking his route, and then the company wanted pictures of him at the beginning of each shift on another app, he told the foundation. But the breaking point came, he told the Thomson Reuters Foundation, when Amazon announced that it would be installing AI cameras in its fleet of vehicles. Insider reported in February that Amazon was equipping all delivery vehicles with AI camera systems called Driveri, manufactured by a company called Netradyne.