One type of researcher writes about AI in education, while another type actually knows what happens when a teacher stands in front of a class and wonders if the new AI tool her school bought for grading is making things better or worse in a quiet way. The second group is made up of Hannah Anush. The Global Recognition Awards this year confirmed what many people in her field had been saying in private for a while: they gave her the 2026 prize in the Research category for a body of work that combines AI, healthcare, and education in a way that is very serious and useful.
It wasn’t just one fancy paper that won the award. It was the result of long-term, steady work. Her most-cited study, which looked at how students and teachers really feel about AI in higher education, has been cited 32 times. More importantly, it gave policymakers evidence, not just assumptions, which is something they don’t often get from academic research. Before that paper, most of the talk about using AI in schools was either driven by excitement or worry, neither of which was based on solid data. Anush took a different route, one that needed time and care, and the field is better for it.
The fact that she won’t stay in one lane makes her scholarship a little different. Her work also includes healthcare, especially AI-powered models for finding diseases early and using natural language processing to look at medical data. That’s a big step away from education, and you don’t make that kind of move if you don’t have a lot of intellectual range. It’s possible that the Global Recognition Awards panel chose her collaborative network as a specific sign of excellence because it takes a multidisciplinary approach. She has worked in engineering, management science, healthcare, and different institutions and places. When someone has that much experience, their research tends to be more complete.
She has 79 citations, an h-index of 5, and an i10-index of 3. These aren’t the most impressive numbers in academic AI research. But academic citation counts aren’t always accurate, especially when it comes to work that is used in real institutional decisions rather than just in journals. When you read Anush’s published work, you get the sense that she’s not just writing for other researchers. Many businesses are reading her work to see if they are ready for what AI is going to ask of them. In almost everything she writes, she talks about how to make institutions ready and how to run them in an ethical way.
The Global Recognition Awards use the Rasch measurement model, which is a set of statistics that lets you compare things accurately across different fields. Anush got the highest score in all five areas that were looked at, including how well it would work in real life. The last one seems to say the most. A lot of AI research gets good grades for originality and method, but the results are never used. Her work on making technology easier to get in developing countries, where the worst effects of using AI without proper oversight are felt the most, shows that she is already thinking about the effects before the paper is even finished.
A representative for the Global Recognition Awards named Alex Sterling said that her research connected theory and practice with a level of clarity and moral seriousness that the panel thought was truly world-class. That kind of statement can sound like it’s been said before, but in this case it fits with what her publication history shows. It’s not an abstract question to ask how to use AI safely in hospitals and schools. If you get it wrong, it could hurt real kids, real patients, and real institutions.
It’s not often that a field slowly recognizes a researcher who has been doing the careful, unglamorous work of building knowledge that can stand up to scrutiny. We live in a time when loudness is rewarded. The 2026 Global Recognition Award for Hannah Anush shows that it is still possible.
