The Promise, Peril, and Possibility of Data, Analytics, and AI in Higher Education (7 of 7): Conclusion and What is Next

Over the last few months, I’ve written a series of short articles highlighting what I deem to be important considerations for higher education institutions as we lean into the emerging and future role of big data, analytics, and AI.

In the first article, I introduced the topic, giving a preview of the what to expect in articles 2 through 6.

In the second article, I focused my reflections and comments on the role of big dat and AI in the pre-enrollment phase, considering the present and future of how learners and colleges will connect. One of the more attention-grabbing contributions in that article was the proposal that we are moving from the metaphor of shopping to the metaphor of dating when it comes to students and colleges finding a mutually beneficial connection.

In article 3, I moved from pre-enrollment to what many would argue is the core of the college experience, learning. What current and emerging technologies will enhance the rate of learning while also creating more personalized and adaptive pathways?

For article 4, I gave attention to student success. If a student gets into college, how can we create the conditions where the student is likely to persist and graduate? Are there early alert systems that might allow us to help students before it is too late? At the same time, what are the dangers with the use of AI in this area, and how we can avoid some of those dangers?

Article 5 broadened the conversation from student to organization. With the number of colleges struggling to remain open and viable, there is growing interest in indicators of overall organizational health. However, as I explain in this article, there are plenty of risks when it comes to building and using organizational health dashboards too.

Having covered the student from pre-enrollment to graduation, while also looking at overall organizational health, I finished the series with an offer for us to think about an emerging and future aspect of big data and AI where forward-thinking higher education institutions might want to experiment. It is one thing to be competent, competent, and a graduate. It is another to build meaningful connections with people and organizations. I have no doubt that big data and AI will be a dominant force in such future connections, but there are ways that we can help students already begin to explore the nature of life, work, learning, and community in a connected age.

As I try to highlight in my articles, there is much already underway when it comes to big data and AI in higher education, but there is so much more to be done. Whether big data and AI will empower learners is partly up to the decisions that we collectively and individually make in the next five to ten years. As such, now is the time for us to advocate for approaches that will lead toward a more hopeful, humane, and self-empowering higher education ecosystem.