The buzz around artificial intelligence (AI) is loud and growing—and for good reason. Maybe you’ve already used an AI tool to transcribe a video call or pore through reams of research to find just the right citation in seconds. If so, you already know how these new AI capabilities can easily accomplish tasks that were just too labor intensive or not technically feasible until now.

One of the most promising opportunities of AI—and a big one that colleges and universities should be laser-focused on—is the ability to create new streams of data and intelligence.

One of the most promising opportunities of AI—and a big one that colleges and universities should be laser-focused on—is the ability to create new streams of data and intelligence. AI has the potential to fast-forward your institution’s transition to data-driven decision-making, so you can move confidently from thought to action with insights that just weren’t possible before.

The Value of Tech That Learns

In the past, we’ve used very basic techniques to catalog and categorize data. As just one example, think about how you measure the strength of your brand. Today marketers can categorize social media posts and tweets in a rudimentary way using keywords to get a feel for how a brand is being received.

This is known as sentiment analysis, and for humans, it is both time-consuming and limiting.

For instance, let’s say you search for sentiment cues using pre-selected keywords (examples: like, love, best, worst, hate). You may be missing other keywords that could have something valuable to tell you. Was the tweet about a competitor? Was that post a feature request? Are you missing a support issue? A compliment? A comparison?

What makes AI so well-suited for tasks like this is machine learning and its flexibility to understand context. Because AI can recognize context, we can now analyze beyond keywords in way we couldn’t before. We can use AI to answer questions like:

  • “What is the key concern?”
  • “What is the primary topic?”
  • “What is the recommended action?”

Collect, Connect and Activate Data in the Age of AI

One of the most exciting opportunities with artificial intelligence in higher education is the ability to create new streams of data that weren’t quantifiable before, giving schools more ways to collect, connect and activate data as intelligence. Here are some examples where AI can make data more impactful across campus:

  • Enrollment data: Collect and analyze data from multiple channels—website analytics, social media metrics and email engagement rates— to understand user intent and reveal patterns in prospect behavior throughout the funnel.
  • Marketing personalization data: Instantly scan and sort incoming emails or chats based on interest in specific schools or majors to deliver targeted and relevant outreach based on individual preferences.
  • Social listening data: Identify and extract sentiment data to measure response to an alumni event or a new program offering.
  • Student performance data: Use data from automated grading to learn how long students spend on certain tasks or what types of questions they struggle with to identify the need for curriculum updates or remedial instruction.
  • Help desk call data: Analyze recorded calls and chats to monitor performance issues and accurately target staff resources and future investment.

Our recommended approach? Start thinking about how AI can expand on the data you already have. These new streams of intelligence can drive new types of insight that simply weren’t functionally (or financially) possible before.

Author: Collegis Education staff

Collegis is passionate about education and driven by the technology that keeps institutions moving forward.