Cockroach Janta party supporters in Mumbai, India, celebrate the resignation of the education minister following protests over an exam paper leak that affected millions of students. Cockroach Janta party supporters in Mumbai, India, celebrate the resignation of the education minister following protests over an exam paper leak that affected millions of students. F amilies with teenagers in education know the private, hidden pain of exam season. But this year, what might have been a summer of quiet family anxiety has erupted in several countries into public unrest. Exam-related turmoil has led to mass student protests in Mexico after nearly 60,000 university applicants were forced to resit tests amid suspected cheating, while Portugal's disastrous attempt to digitise school exam marking sparked the country's worst education crisis in decades. By far the largest and most ground-shaking incident was in India, however, where an exam paper leak affected millions of students and was linked to more than a dozen students taking their own lives.

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When two OpenAI models hacked into the website Hugging Face in July, they weren't trying to make money or commit sabotage--they were just looking for answers to a test question. According to a postmortem from OpenAI, the models, which had been stripped of their typical security features for testing, decided to solve a cybersecurity exercise by hacking out of the isolated environment in which OpenAI had attempted to contain them and into Hugging Face's databases, where--they reasoned--the correct answer to the problem might be stored. The Hugging Face incident has attracted intense attention over the past couple of weeks. It's a dramatic illustration of just how good AI models have gotten at hacking: In order to get into Hugging Face's databases, the models had to string together several previously undiscovered cybersecurity exploits. But it's perhaps even more striking as an example of how and why AI systems lie and cheat. And as models get increasingly powerful, the consequences could get far more severe.

Things to Do in L.A. Tap to enable a layout that focuses on the article. California DMV's test fraud claims spark frustration, anger People wait in line to enter the DMV office off Waterman Avenue in San Bernardino on Wednesday. The agency says some drivers are suspected of using "various cheating methods" on the written portion of the license test. This is read by an automated voice. Please report any issues or inconsistencies here .

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The recent success of generative AI highlights the crucial role of high-quality human feedback in building trustworthy AI systems. However, the increasing use of large language models (LLMs) by crowdsourcing workers poses a significant challenge: datasets intended to reflect human input may be compromised by LLM-generated responses. Existing LLM detection approaches often rely on high-dimensional training data such as text, making them unsuitable for structured annotation tasks like multiple-choice labeling. In this work, we investigate the potential of peer prediction --- a mechanism that evaluates the information within workers' responses --- to mitigate LLM-assisted cheating in crowdsourcing with a focus on annotation tasks.

Public debate about artificial intelligence in higher education has largely orbited a familiar worry: cheating . Will students use chatbots to write essays? Should universities ban the tech? But focusing so much on cheating misses the larger transformation already underway, one that extends far beyond student misconduct and even the classroom. Universities are adopting AI across many areas of institutional life .

It isn't always easy to detect cheating in chess Just three judiciously deployed cheats can turn an otherwise equal chess game into a near-certain victory, a new analysis shows - and systems designed to crack down on cheating might not notice the foul play. Daniel Keren at the University of Haifa in Israel simulated 100,000 matches using the powerful Stockfish chess engine - a computer system that, at its maximum power, is better at playing chess than any human world champion. The matches were played between two computer engines competing at the level of an average chess player - 1500 on the Elo rating scale typically used to calculate skill level in chess. Half the games were logged without any further intervention, while the other half allowed occasional intervention by a stronger computer chess "player" with an Elo score of 3190 - a higher rating than any human player has ever achieved. Competitors usually have a slim advantage when playing white, with a 51 per cent chance of winning, on average, tied to the fact that they make the game's first move.

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.

As artificial intelligence (AI) becomes increasingly integrated into education, understanding how students perceive its risks is essential for supporting responsible and effective adoption. This research aimed to examine the relationships between perceived AI competence and risks among Finnish K-12 upper secondary students (n = 163) by utilizing a co-occurrence analysis. Students reported their self-perceived AI competence and concerns related to AI across systemic, institutional, and personal domains. The findings showed that students with lower competence emphasized personal and learning-related risks, such as reduced creativity, lack of critical thinking, and misuse, whereas higher-competence students focused more on systemic and institutional risks, including bias, inaccuracy, and cheating. These differences suggest that students' self-reported AI competence is related to how they evaluate both the risks and opportunities associated with artificial intelligence in education (AIED). The results of this study highlight the need for educational institutions to incorporate AI literacy into their curricula, provide teacher guidance, and inform policy development to ensure personalized opportunities for utilization and equitable integration of AI into K-12 education.