Reference to artificial intelligence (AI) has become strategic in higher-ed discourse, joining the terms “big data” and “predictive modeling.” When I was introduced to AI in 2013 by a member of our design team, it captivated my imagination. Since then, as our data grew to proportions that were ripe for AI, I’ve become enthralled by its potential to enrich the accuracy and personalization that leads to better outcomes. That does not make me an expert.

If anything, it could make me equal to all out there who have wondered what these terms actually mean, how they matter to education, and where to draw the line between hype and results.

Defining the terms

Artificial intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart.”

Machine learning (ML) is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves (Bernard Marr, author, speaker).

Note: Neither definition implies the machine outsmarts or replaces its human team.

How #AI can be used to make a difference in #highered

AI in healthcare

Like higher ed, healthcare systems are complex, tethered to random human behavior, constantly evolving, and looking to technology to help improve outcomes for all. I found the following healthcare example helpful for visualizing how AI makes it possible to take human expertise and replicate it at scale.

In a healthcare experiment, AI scientists worked with experts at diagnosing a certain type of lung cancer. Collectively, these teams converted expert knowledge into a set of rules and decision trees for reading a lung cancer X-ray and determining diagnosis. In the end, the machine “student” outperformed the very experts who designed its rules.

For some people this outcome seems obvious and acceptable. For others, the thought of relying on a machine diagnosis over a trusted doctor is not acceptable. The reason the machine outperforms the human, while following the same rules, is that the machine is free from bias, second-guessing, fatigue, or distraction. The AI machine becomes a critical member of the team—not a replacement.

About the Author:

Elena Cox is chief executive officer/co-founder of vibeffect. Launched in 2013, vibeffect® is the company that is turning student success into a science, and has built a full student success model, complete with a Thriving Recommender Engine and proprietary micro-guidance system. Over the past 20 years, Cox has worked nationally and internationally to promote philanthropy, learn from innovative corporate social responsibility projects, and raise funds for both non-profit and for-profit ventures.


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