Everyone has a stake in how tech is shaping education today. From the tech moguls and venture capitalists who are starting "microschools" and building ed-tech tools to policymakers who are writing bills to safeguard kids online and teachers who are getting creative about using AI for school. WIRED explored all this and more in our recent back-to-school digital edition, and we're excited to talk about it at our next subscriber-only livestream on Thursday, August 28, at 1 pm ET / 10 am PT / 6 pm UK. We'll talk about what we learned--turns out that AI has given new life to a skill many people thought would be extinct soon, and one country is attempting to actually ban social media for teenagers--but we also want to hear from you. Share your stories and questions for us via this form, or leave them in the comments below.

Yesterday, we covered Google's report that a typical query to its Gemini AI consumes only "five drops of water." That figure is now facing criticism from several AI experts, according to The Verge… and that includes one of the authors of one of the reports referred to by Google. AI researcher Shaolei Ren--a professor at University of California Riverside and one of the authors of the report Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models--previously estimated that Microsoft's data center consumed 700,000 liters of water to train OpenAI's GPT-3 model. He also calculated that a ChatGPT conversation of 20 to 50 messages can consume close to a pint of water, which is far more than Google's estimate. Ren and other AI researchers argue that Google is wrong to leave out the indirect water consumption of its AI models.

Oral assessments, more security checks and speedier marking are all on the cards as generative artificial intelligence (AI) could transform exams for the next generation of students. As the 2025 exam season drew to a close with GCSE students picking up their results on Thursday, after mostly sitting traditional pen and paper exams, AI is already changing the landscape. Exam preparation is undergoing a revolution, with students increasingly creating personal AI tutors, available around the clock to generate learning materials to suit individual needs that potentially lead to better results. "Using AI can give a student a much better understanding of a subject because they can ask those questions they wouldn't ask in class, or at odd hours, without being judged," said Dr Andrew Rogoyski of the Surrey Institute for People-Centred AI. "It really took off this summer," said Sandra Leaton Gray, a professor of education futures at University College London's Institute of Education. "So they're able to talk to it about the marking frameworks that are in use and upload those, and then they're able to do sample answers on their own. And then they're able to say to the AI: 'How would you improve the answer?' It's like having a tireless tutor."

Google has just released a report detailing how much energy its Gemini apps use for each query. In total, the median prompt--one that falls in the middle of the range of energy demand--consumes 0.24 watt-hours of electricity, the equivalent of running a standard microwave for about one second. The company also provided average estimates for the water consumption (five drops per query) and carbon emissions associated with a text prompt to Gemini. It's the most transparent estimate yet from a Big Tech company with a popular AI product, and the report includes detailed information about how the company calculated its final estimate. Earlier this year, MIT Technology Review published a comprehensive series on AI and energy, at which time none of the major AI companies would reveal their per-prompt energy usage.

They make counterfactual predictions by using observed actions to learn the underlying utility function (a.k.a.

Children are remarkably fast in learning new skills; How do they do this?

The framework of feedback graphs is a generalization of sequential decision-making with bandit or full information feedback. In this work, we study an extension where the directed feedback graph is stochastic, following a distribution similar to the classical Erdős-Rényi model. Specifically, in each round every edge in the graph is either realized or not with a distinct probability for each edge.

The single-step formulation does not account for changes in the student's internal state over In the multi-step formulation, effort put towards studying accumulates in the form of knowledge. We demonstrate this by revisiting the classroom example. The student's grade is then the summation of all scores across time. B.1 Agent's best-response effort sequence A rational agent solves the following optimization to determine his best-response effort policy: { e Recall that the agent's score A dominated effort policy is formally defined as follows: Lemma C.1 Next we look at the complementary slackness condition. From Lemma D.1, we know the form a rational agent's effort Substituting this into Equation 6, we obtain the following characterization of the principal's assessment policy: { E.1 The set of incentivizable effort policies is convex Proof.

A recent line of research investigates how strategic agents may respond to such scoring tools to receive favorable assessments. While prior work has focused on the short-term strategic interactions between a decision-making institution (modeled as a principal) and individual decision-subjects (modeled as agents), we investigate interactions spanning multiple time-steps . In particular, we consider settings in which the agent's effort investment