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Professor Roberto Serrano at Brown University detected at least 50 students cheating using AI on a midterm exam, sparking a debate about academic integrity in higher education.
Medical students are reportedly using a popular research tool to produce misleading studies, raising concerns about academic integrity and research quality.
A student expresses frustration that their entirely human-written paper was flagged as AI-generated by plagiarism checkers, highlighting the flaws of current AI detection tools in academic settings.
A French physicist and media personality had their doctorate revoked following an investigation into plagiarism.
This research paper demonstrates that large language models produce correlated name ensembles (e.g., Elena Vasquez and Marcus Chen for Claude) that appear across independently generated documents, and reveals that these ghost names have infiltrated academic repositories like Zenodo, with 1,655 fake records minting real DOIs.
Over 150 mathematicians signed a declaration warning governments against overestimating AI capabilities, citing exaggerated claims by companies like OpenAI and urging regulation of AI in sensitive areas.
A report from UC Berkeley shows that failing grades in computer science courses have surged due to increased AI use and weaker math skills among students. Instructors attribute the trend to academic dishonesty and lack of preparation, with failure rates far exceeding typical department guidelines.
NeurIPS 2026 used a proprietary AI-text detector to desk-reject papers for alleged AI policy violations without validating it on the target distribution; the same detector later flagged conference chairs' own papers as likely AI-written.
The article discusses the surprising backlash against Arxiv's proposed one-year ban for authors who submit papers with hallucinated references from LLMs, highlighting revealing responses from academics.
arXiv announces a 1-year ban for authors whose papers contain unchecked LLM-generated errors like hallucinated references, emphasizing author responsibility for all content regardless of generation method.
arXiv announces a new policy imposing a 1-year ban for authors who include hallucinated references, emphasizing that authors are fully responsible for paper contents regardless of how they were generated.
Princeton faculty voted to require proctoring for in-person exams, ending a 133-year-old honor system tradition, citing the rise of AI and personal electronic devices as major factors in increased cheating.
This paper introduces MELD, a detector for AI-generated text that uses multi-task learning with auxiliary heads for generator family, attack type, and source domain to improve robustness. MELD achieves strong performance on the RAID benchmark and maintains low false-positive rates under adversarial attacks.
LLMSniffer is a detection framework that fine-tunes GraphCodeBERT with supervised contrastive learning to distinguish AI-generated code from human-written code, achieving 78% accuracy on GPTSniffer and 94.65% on Whodunit benchmarks. The approach addresses critical challenges in academic integrity and code quality assurance by combining code-structure-aware embeddings with contrastive learning and comment removal preprocessing.
OpenAI publishes a guide for students on using ChatGPT responsibly to enhance writing and thinking skills while maintaining academic integrity, emphasizing transparency and proper citation of AI usage.