Jan 10 2024

Getting Your Higher Education Infrastructure AI-Ready

Tools powered by artificial intelligence are already flooding the technology market. Here’s how to make sure you’re ready when those tools arrive.

In the world of artificial intelligence, the days before ChatGPT seem like eons ago.

Of course, OpenAI unleashed ChatGPT just over a year ago, in late 2022. But the time since has been filled with conversation, consternation, deliberation and a little bit of inspiration, as people around the world grapple with what the future of AI will look like.

In higher education, AI might be a fresh topic in some corners of campus, but it’s not so new in others, especially at research institutions. For years, colleges and universities have been at the forefront of AI development. Projects like the Jill Watson AI tutor, created by Georgia Tech and IBM back in 2016, and Georgia State University’s innovative chatbot Pounce, which has had a significant impact on student engagement by reducing summer melt, have helped lead the way.

Beyond those examples, AI has found its way into research in biochemistry, pharmacology, meteorology and other areas on campus where researchers are looking to derive insights from large data sets. That’s something that will likely increase as a wave of grants are awarded on the heels of the exploding interest in AI.

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Still, the AI craze has prompted understandable questions at universities among students, faculty members and administrators, all wondering whether generative AI tools like ChatGPT will spur a wave a plagiarism, lead to job loss or otherwise disrupt college life. The answer, unsatisfying as it may be, is that AI might do all of those things, but no one really knows how. And even if they did know how, things could change quickly.

The great thing about AI — and the problem with AI — is that it could impact literally everything, which makes it hard to know where to start.

What Should Higher Education Institutions Do About AI?

There has been a lot of thinking and talking about AI in higher education, but little has been done about it, and that’s not necessarily a bad thing.

In some sectors, there are obvious applications for AI technology and even generative AI tools. In pharmacology and life sciences, for example, huge amounts of data can be analyzed to make important health-related recommendations and decisions. In the financial services world, crunching numbers more quickly and in a more insightful way than the competition is part of the job. And in retail, where AI chatbots are able to provide more helpful customer service and reduce fraud, their implementation is a no-brainer.

The thing to remember is that the people who fully understand what to do with AI are the outliers. Most of the rest of us, including in higher education, are still figuring this stuff out. Diving too soon into AI implementations could mean being the first to make a mistake, and with personal data at stake and major ethical questions still unresolved, there are few institutions willing to go first. Most are wisely keeping their ear to the ground and hoping to be a fast follower behind someone with less to lose.

That said, it would be a mistake to pretend as though the AI future is not on the horizon. Especially now, with the number of resources and amount of brainpower being devoted to AI, it’s a matter of when, not if, college campuses are inundated with AI-powered tools helping with everything from student success to physical security and IT operations.

With that in mind, here are at least two areas where colleges and universities should be laying the groundwork now so they’re ready when the future of AI arrives.

RELATED: AI is streamlining university contact center operations.

Pave the Road for Future AI Tools with Infrastructure Upgrades

Higher education institutions will need to have a certain AI footprint in the future, and for most, that footprint is going to require some computing upgrades. Universities doing research likely already need some type of high-performance, GPU-enabled computing, but that computing power may soon be necessary outside of that research.

In addition, current higher education networks are built for moving data in a pre-AI world, and that’s not going to cut it going forward. Institutions will need more bandwidth and lower latency on their networks to make AI tools generate the outcomes they want, in a hurry. Network demands will be a barrier to AI implementation for institutions that don’t act soon.

There will also be an unprecedented amount of data to manage. AI tools not only need a large pool of data to analyze, but also generate their own data that will need to be stored somewhere, likely in partnership with a cloud provider.

What scale of network and data center upgrades colleges and universities will require depends on the institution and a host of other considerations. At CDW, our Mastering Operational AI Transformation program can help guide institutions in mapping out their AI strategy. Regardless of the institution, assessing a university’s networking and storage capabilities — and potential for growth and expansion — is a great first step.

This article is part of EdTech: Focus on Higher Education’s UniversITy blog series.

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