When a student’s work sparks concerns over AI use, the best approach is to sit down with them and have a conversation to ascertain if they understand what they submitted, explains B. Jean Mandernach, who shares tips for doing this
With practical tools, early career researchers can build productive partnerships that advance their work and withstand the pressures of shrinking budgets and shifting priorities
Trying to detect whether a student has misused AI in their work is a wasted effort, from which no one benefits, writes B. Jean Mandernach. She proposes a different approach focused on finding out what students truly understand
Inspiring future generations of STEM scholars demands more than just a one-time introduction to science or engineering. Lasting impact comes from ongoing learning experiences, mentorship and institutional support, writes Keisha Simmons
How can universities develop sophisticated systems for data sharing and analysis that can guide communications and student services? Alain Pompilus was tasked with putting such a system in place and shares lessons from the experience
When it comes to improving green practices across a university’s laboratories, meaningful change doesn’t always require major investment or new infrastructure. Small but intentional practices can yield substantial results
With higher education navigating rapid technological change, the key to embedding AI literacy in the workforce of tomorrow could be a focus on collaboration over competition
An LGBTQ+ scientist explains how funding cuts as a result of the US administration’s attacks on DEI-related research gave rise to a ‘gonzo science’ project and why data collection is its own form of resistance
The question is no longer whether students will use AI after graduation but to what extent. So, how can universities best ensure that students are workforce-ready?
