A BBC investigation has uncovered claims that essay cheating remains widespread at UK universities despite the introduction of a law designed to stop it. Since April 2022, it has been illegal to provide essays for students in post-16 education in England. But so far there have been no prosecutions. The BBC has spoken to a former lecturer who describes essay cheating as an open secret and to a businessman who claims to have made millions from selling model answer essays to university students. Universities UK, which represents 141 institutions, said there were severe penalties for students caught submitting work that was not their own.

Effective communication is central to achieving positive healthcare outcomes in mental health contexts, yet international students often face linguistic and cultural barriers that hinder their communication of mental distress. In this study, we evaluate the effectiveness of AI-generated images in supporting self-expression of mental distress. To achieve this, twenty Chinese international students studying at UK universities were invited to describe their personal experiences of mental distress. These descriptions were elaborated using GPT-4o with four persona-based prompt templates rooted in contemporary counselling practice to generate corresponding images. Participants then evaluated the helpfulness of generated images in facilitating the expression of their feelings based on their original descriptions. The resulting dataset comprises 100 textual descriptions of mental distress, 400 generated images, and corresponding human evaluation scores. Findings indicate that prompt design substantially affects perceived helpfulness, with the illustrator persona achieving the highest ratings. This work introduces the first publicly available text-to-image evaluation dataset with human judgment scores in the mental health domain, offering valuable resources for image evaluation, reinforcement learning with human feedback, and multi-modal research on mental health communication.

How Trump's policies are affecting early-career scientists--in their own words Every year, we recognize extraordinary young researchers on our Innovators Under 35 list. Recent honorees told us how they're faring under the new administration. Every year celebrates accomplished young scientists, entrepreneurs, and inventors from around the world in our Innovators Under 35 list . We've just published the 2025 edition . This year, though, the context is pointedly different: The US scientific community finds itself in an unprecedented position, with the very foundation of its work under attack . Since Donald Trump took office in January, his administration has fired top government scientists, targeted universities individually and academia more broadly, and made substantial funding cuts to the country's science and technology infrastructure .

The Trump administration on Wednesday announced a proposed rule to limit the length of time international students can remain in the U.S. for their studies to four years. If finalized, the proposed rule set to be published on Thursday would limit how long certain visa holders, including foreign students, are allowed to stay in the U.S., according to a press release from the Department of Homeland Security, which said the proposal seeks to curb "visa abuse" and increase the agency's ability to "properly vet and oversee these individuals." The agency said foreign students have "taken advantage of U.S. generosity" and become "forever students" by remaining enrolled in colleges so they could stay in the U.S. The Trump administration announced a proposed rule to limit the length of time international students can remain in the U.S. for their studies to four years. "For too long, past Administrations have allowed foreign students and other visa holders to remain in the U.S. virtually indefinitely, posing safety risks, costing untold amount of taxpayer dollars, and disadvantaging U.S. citizens," a DHS spokesperson said in a statement. "This new proposed rule would end that abuse once and for all by limiting the amount of time certain visa holders are allowed to remain in the U.S., easing the burden on the federal government to properly oversee foreign students and their history," the spokesperson continued. Since 1978, foreign students, or F visa holders, have been allowed to remain in the U.S. for their "duration of status," which means the time they were enrolled as a full-time student.

The US administration's targeting of academic research and international students is a "great gift" to China in the race to compete on artificial intelligence, former OpenAI board member Helen Toner has said. The director of strategy at Georgetown's Center for Security and Emerging Technology (CSET) joined the board of OpenAI in 2021 after a career studying AI and the relationship between the United States and China. Toner, a 33-year-old University of Melbourne graduate, was on the board for two years until a falling out with founder Sam Altman in 2023. Altman was fired by the board over claims that he was not "consistently candid" in his communications and the board did not have confidence in Altman's ability to lead. The chaotic months that followed saw Altman fired and then re-hired with three members of the board, including Toner, ousted instead.

Around midnight on April 16, 2025, after Chen Zimo learned that the Department of Homeland Security had threatened to revoke Harvard University's certification to enroll international students, he began communicating with a trusted source about possible legal scenarios. Chen, a Chinese citizen, still needed a number of courses before he could complete his degree in computer science at Harvard, and he felt panicked about the possibility of having his visa revoked. For him, the Harvard experience had been transformative. Chen--not his real name--had grown up in provincial China, where his family had modest resources and sent him to public schools. He could never have afforded Harvard without the university's generous financial support, and he had also received funding for summer language study.

Secretary of State Marco Rubio said Wednesday that the US plans to "aggressively revoke" the visas of Chinese students, including those working in critical fields or with ties to the Chinese Communist Party. Experts warn the move--along with the Trump administration's broader crackdown on international students--could drain American scientific labs of top STEM talent and upend cutting-edge research in areas like artificial intelligence. "If you were aiming to help China beat the US at AI, the first thing you would do is disrupt the flow of top talent from all around the world into the US," says Helen Toner, director of strategy and foundational research grants at Georgetown University's Center for Security and Emerging Technology. While it has a population only about a quarter the size of China, "the US has had a huge asymmetric advantage in attracting the cream of the global crop," she adds. Several close Trump allies, including Elon Musk, have argued that attracting the best engineers from around the world is essential for the US to maintain its technological dominance.

While large language models excel at following explicit instructions, they often struggle with ambiguous or incomplete user requests, defaulting to verbose, generic responses rather than seeking clarification. We introduce InfoQuest, a multi-turn chat benchmark designed to evaluate how dialogue agents handle hidden context in open-ended user requests. The benchmark presents intentionally ambiguous scenarios that require models to engage in information-seeking dialogue through clarifying questions before providing appropriate responses. Our evaluation of both open and closed-source models reveals that while proprietary models generally perform better, all current assistants struggle with effectively gathering critical information, often requiring multiple turns to infer user intent and frequently defaulting to generic responses without proper clarification. We provide a systematic methodology for generating diverse scenarios and evaluating models' information-seeking capabilities, offering insights into the current limitations of language models in handling ambiguous requests through multi-turn interactions.

This study builds on person perception and human AI interaction (HAII) theories to investigate how content and source cues, specifically race, ethnicity, and nationality, affect judgments of AI-generated content in a high-stakes self-presentation context: college applications. Results of a pre-registered experiment with a nationally representative U.S. sample (N = 644) show that content heuristics, such as linguistic style, played a dominant role in AI detection. Source heuristics, such as nationality, also emerged as a significant factor, with international students more likely to be perceived as using AI, especially when their statements included AI-sounding features. Interestingly, Asian and Hispanic applicants were more likely to be judged as AI users when labeled as domestic students, suggesting interactions between racial stereotypes and AI detection. AI attribution led to lower perceptions of personal statement quality and authenticity, as well as negative evaluations of the applicant's competence, sociability, morality, and future success.

In this course, we will learn how a human-robot interaction course at an undergraduate liberal robots use computational models to have natural and intuitive social arts college. We provide a sample syllabus adapted from a previous interactions with humans.