A scientific review published today in the NEJM Evidence journal, coordinated by the D’Or Institute for Research and Education (IDOR), evaluated outcomes of adults with community-acquired pneumonia (CAP) admitted to intensive care units (ICUs) in middle-income countries.

In contrast to high-income countries, where mortality ranges from 16% to 26%, the study found significantly higher rates in the countries analyzed. The work brought together 52 studies and approximately 48,707 patients, revealing an overall mortality rate of 37.1%, which increased to 59.3% among patients requiring respiratory support.

This study in rural Spain evaluated the effectiveness of a program called the School of Caring, a nine-session weekly group program for informal caregivers of people with dementia. The program combined dementia education, psychological support, and guidance on community resources for 132 informal caregivers.

A new article explores what constitutes quality teaching in the age of AI and argues that emotional intelligence, creativity, and professional judgment remain central to effective education.

Reflective practice is often seen as a golden standard in teacher development. This philosophical critique, drawing on Foucault and Aristotle, shows that reflection has been disenchanted from an antique ethics of care of the self into a secularised epistemology, a techne for production, and even a neoliberal governance technology. The authors call for re‑enchanting reflective practice as phronesis that is a three‑dimensional virtue ethics and for teacher flourishing as students' in loco parentis.

Okayama University of Science was ranked in the 801+ category of the 2026 Times Higher Education Asia University Rankings, marking its second consecutive year in the rankings. The university was the only private institution from Japan’s Chugoku and Shikoku regions to be included and tied for 8th place among private universities west of Kansai.

Top AI systems only match human examiners for grade bands when marking undergraduate essays around half the time, study finds. AI shows bias towards rewarding complex prose styles regardless of academic quality.

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.