Higher education must rethink assessment practices in response to the growing integrity challenges posed by generative artificial intelligence (GenAI), say authors in this Policy Forum. They analyzed data on student use of this technology across 20 major public research universities in the United States. The impact of GenAI on higher education is highly debated. In many ways, the technology is making common forms of evaluation, such as tests, projects, or term papers, less reliable as a measure of student capability. This highlights the need for a better understanding of where GenAI use is most prevalent and where misuse is most likely to occur. Igor Chirikov and colleagues analyzed survey data from more than 95,000 students across 20 U.S. research universities during the 2023-2024 academic year. Their findings reveal widespread GenAI use among students: roughly two-thirds of students reported using it over the study period, with 37% using it regularly. However, usage patterns differed considerably by discipline, with higher adoption in STEM fields. For example, 62% of computer science students reported regular usage, compared with only 24% of students in the arts. Notably, some social science disciplines, such as business and economics, also demonstrated high levels of adoption. Patterns of GenAI-assisted cheating also varied across disciplines. Estimated rates of misuse were generally higher in non-STEM fields, with economics (17%) and journalism (16%) showing relatively high rates, whereas biology (5%) was among the lowest. The study also found significant demographic disparities in GenAI use, with higher adoption among male, White, and Asian students than among female and underrepresented minority students. Although differences tied to socioeconomic status and disability were smaller, the authors suggest that the findings raise concerns about unequal access to AI tools and literacy. Chirikov et al. propose several paths forward. They note that there is no single “AI-proof” assessment model and suggest reforms tailored to individual disciplines. They place a focus on preparing students to use AI responsibly in professional contexts.

Despite distinct archaeological differences, the people from the Western Funnel Beaker culture were more closely related to neighboring Wartberg communities than previously understood, according to a genomic study of German burial sites. The findings reveal surprisingly long-distance family ties – including a father/son pair buried over 200 kilometers apart – suggesting that kinship, mobility, and social exchange connected megalithic populations across great distances. During the Late Neolithic (~4500 to 2800 BCE), megalithic monuments – large stone structures – emerged across Europe. These architectural works reflected local traditions while also hinting at far-reaching social, cultural, or ancestral ties between distant populations. One megalithic society, the Western Funnel Beaker culture (TRB-west) of north-central Europe, is known for its elaborate stone burial chambers and other distinct traditions. However, there has been little genetic evidence available that helps clarify how TRB-West people were related to neighboring megalithic communities. The only TRB-West site where well-preserved human remains have been recovered to date is Sorsum, located in present-day Germany. This site has megalithic features that resemble the nearby Wartberg culture (WBC).

 

To investigate whether the archaeological similarities also reflect kinship between these groups, Nicolas Antonio da Silva and colleagues analyzed the genomes of 203 individuals (including 129 newly generated genomes) from the Sorsum site and five WBC sites. The results revealed that the people buried at Sorsum were genetically far more closely related with WBC groups than other TRB-west groups, despite their differing archaeological labels. Both Sorsum and WBC individuals also shared unusually high levels of ancestry from western hunter-gatherers, particularly through male lineages, suggesting deep and sustained biological connections. Notably, de Silva et al. discovered striking evidence of long-distance kinship networks, including a biological father and son buried at sites separated by 225 kilometers (the WBC site of Niedertiefenbach and Sorsum, respectively). Other second-degree biological relationships were also identified between Sorsum and distant WBC sites, suggesting occasional movement, intermarriage, or social and cultural exchange across large geographic areas for the time. Yet both regions lack close genetic ties with more distant megalithic populations in the British Isles or Scandinavia to the north. This indicates that megalithic traditions likely spread culturally, rather than through biological networks. The authors argue that Sorsum and the WBC communities formed a genetically continuous population, despite some marked differences in material culture. Sorsum may have represented a northern branch of the WBC world that adopted certain TRB-west traditions while remaining biologically and socially connected to the neighboring WBC groups.

 

For reporters interested in issues in research integrity, co-author Ben Krause-Kyora remarks: “In ancient DNA research, increasing emphasis has been placed on authentication standards, reproducibility, open data sharing, and contamination control. Community-wide adoption of transparent bioinformatic pipelines and independent replication has substantially strengthened confidence in results. Moving forward, stronger support for long-term data accessibility, standardized metadata reporting, and interdisciplinary validation approaches would further enhance research integrity across the field.”

Large numbers of college students are now using artificial intelligence to complete – and cheat on – their assignments, suggesting that colleges and universities need to change how they are evaluating students, new Cornell University research finds.

In a new study published in Sustainability, researchers surveyed Rwandan young adults on their financial knowledge, skills, and behaviors to identify gaps in their DFL. They found modest strengths in some areas and gaps in others, as well as disparities by gender and education.

Scientists at the Icahn School of Medicine at Mount Sinai have created a new artificial intelligence (AI) model that helps reveal how genes function together inside human cells, offering a powerful new way to understand biology and disease. The study, published in the May 21 online issue of Patterns, a Cell Press Journal [DOI: https://doi.org/10.1016/j.patter.2026.101565], introduces a gene set foundation model (GSFM) designed to learn patterns in how genes are grouped and function across thousands of biological contexts. The work draws inspiration from advances in large language models (LLMs) such as ChatGPT, which learn how words gain meaning depending on their context. In a similar way, a GSFM learns how genes behave differently depending on their cellular “context.”