The National Police Agency plans to warn against the obscene use of AI at delinquency-prevention lectures at schools and other events. More than half of cases reported to Japanese police of explicit deepfakes targeting those aged under 18 were created with the involvement of students from the same schools as the victims, National Police Agency data have shown. This is the first time that the NPA has released information on minors who became victims of obscene fake images created using generative artificial intelligence and other technologies. The agency plans to create flyers and warn against such use of AI at delinquency-prevention lectures at schools and other locations. According to the NPA, police were consulted over 79 cases of deepfakes targeting those up to the age of 17 from January to September this year.

Science Space Solar System Mars NASA's Mars Reconnaissance Orbiter snaps 100,000th image A high school student suggested the steep sand dunes of Syrtis Major for the milestone image. Breakthroughs, discoveries, and DIY tips sent every weekday. NASA's Mars Reconnaissance Orbiter (MRO) officially went into service above the Red Planet in November 2006. The spacecraft has since spent nearly 20 years circling Earth's closest neighbor, studying its geology and identifying icy evidence of a once watery world . After already sending back more than 450 terabits of data over the course of its ongoing mission, the orbiter recently passed a major milestone: its 100,000th image of the Martian surface.

Although persona prompting in large language models appears to trigger different styles of generated text, it is unclear whether these translate into measurable behavioral differences, much less whether they affect decision-making in an adversarial strategic environment that we provide as open-source. We investigate the impact of persona prompting on strategic performance in PERIL, a world-domination board game. Specifically, we compare the effectiveness of persona-derived heuristic strategies to those chosen manually. Our findings reveal that certain personas associated with strategic thinking improve game performance, but only when a mediator is used to translate personas into heuristic values. We introduce this mediator as a structured translation process, inspired by exploratory factor analysis, that maps LLM-generated inventory responses into heuristics. Results indicate our method enhances heuristic reliability and face validity compared to directly inferred heuristics, allowing us to better study the effect of persona types on decision making. These insights advance our understanding of how persona prompting influences LLM-based decision-making and propose a heuristic generation method that applies psychometric principles to LLMs.

--As large language models (LLMs) become more common in educational tools and programming environments, questions arise about how these systems should interact with users. This study investigates how different interaction styles with ChatGPT -4o (passive, proactive, and collaborative) affect user performance on simple programming tasks. I conducted a within-subjects experiment where fifteen high school students participated, completing three problems under three distinct versions of the model. Each version was designed to represent a specific style of AI support: responding only when asked, offering suggestions automatically, or engaging the user in back-and-forth dialogue.Quantitative analysis revealed that the collaborative interaction style significantly improved task completion time compared to the passive and proactive conditions. Participants also reported higher satisfaction and perceived helpfulness when working with the collaborative version. These findings suggest that the way an LLM communicates, how it guides, prompts, and responds, can meaningfully impact learning and performance.This research highlights the importance of designing LLMs that go beyond functional correctness to support more interactive, adaptive, and user-centered experiences, especially for novice programmers. Large language models (LLMs) are rapidly changing how people learn to code.

Tokyo police have arrested three teenage boys on suspicion of fraudulently subscribing to Rakuten Mobile's phone service via a self-made program using artificial intelligence. The Metropolitan Police Department's cybercrime unit believes that the boys obtained at least about 2,500 mobile phone subscriptions in about six months from December 2023 and sold them for a total of about 7.5 million in crypto assets. The arrests were made for allegedly obtaining 105 mobile phone subscriptions between May and August last year by logging into the Rakuten Mobile system with other people's IDs and passwords. The boys -- a 14-year-old third-year junior high school student in Tokyo, a 16-year-old first-year high school student in Gifu Prefecture and a 15-year-old third-year junior high school student in Shiga Prefecture -- have admitted to the allegations, according to police sources. One of the three was quoted as saying that he wanted to attract attention on social media by devising and carrying out a sophisticated criminal scheme.

The impressive essay writing and problem-solving capabilities of large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education. Our goal is to gain insights into the widespread use of LLMs among secondary students to inform their future development. Despite school restrictions, our survey of over 300 middle and high school students revealed that a remarkable 70% of students have utilized LLMs, higher than the usage percentage among young adults, and this percentage remains consistent across 7th to 12th grade. Students also reported using LLMs for multiple subjects, including language arts, history, and math assignments, but expressed mixed thoughts on their effectiveness due to occasional hallucinations in historical contexts and incorrect answers for lack of rigorous reasoning. The survey feedback called for LLMs better adapted for students, and also raised questions to developers and educators on how to help students from underserved communities leverage LLMs' capabilities for equal access to advanced education resources. We propose a few ideas to address such issues, including subject-specific models, personalized learning, and AI classrooms.

But the news item that really stood out to me was one that didn't get as much attention as it should have. It has the potential to usher in more powerful AI and scientific discovery than previously possible. Last Thursday, Google DeepMind announced it had built AI systems that can solve complex math problems. The systems--called AlphaProof and AlphaGeometry 2--worked together to successfully solve four out of six problems from this year's International Mathematical Olympiad, a prestigious competition for high school students. Their performance was the equivalent of winning a silver medal.

Using large language models (LLMs) for educational applications like dialogue-based teaching is a hot topic. Effective teaching, however, requires teachers to adapt the difficulty of content and explanations to the education level of their students. Even the best LLMs today struggle to do this well. If we want to improve LLMs on this adaptation task, we need to be able to measure adaptation success reliably. However, current Static metrics for text difficulty, like the Flesch-Kincaid Reading Ease score, are known to be crude and brittle. We, therefore, introduce and evaluate a new set of Prompt-based metrics for text difficulty. Based on a user study, we create Prompt-based metrics as inputs for LLMs. They leverage LLM's general language understanding capabilities to capture more abstract and complex features than Static metrics. Regression experiments show that adding our Prompt-based metrics significantly improves text difficulty classification over Static metrics alone. Our results demonstrate the promise of using LLMs to evaluate text adaptation to different education levels.

The Beverly Hills Unified School District says the laws are still catching up with the technology. Multiple Los Angeles-area school districts have investigated instances of "inappropriate," artificial intelligence-generated images of students circulating online and in text messages in recent months. Most recently, the Los Angeles Unified School District (LAUSD) announced that it is investigating "allegations of inappropriate photos being created and disseminated within the Fairfax High School community," the school district told Fox News Digital in a statement. "These allegations are taken seriously, do not reflect the values of the Los Angeles Unified community and will result in appropriate disciplinary action if warranted." A preliminary investigation revealed that the images were allegedly "created and shared on a third-party messaging app unaffiliated with" LAUSD.

Artificial intelligence can already write poems and make movie recommendations. What's it going to do next? High school students in Longmont, Colorado, are learning how to design their own AI model projects at the St. Vrain Valley School District Innovation Center. The program started this past fall. Mai Vu, the A.I. Program manager at St. Vrain Valley School District, said the AI program's goal is to teach students how to use AI to solve real-world problems.