The global higher education community is in the midst of irreversible changes to the learning experience brought on by the pandemic. Blended classrooms and hybrid learning are here to stay. But in the fallout of the pandemic, longer-term consequences pose a serious threat.
Recent McKinsey research examining higher education finds that the risk of “outcome inequities” for students could worsen in the U.S., impacting completion, employment, and lifetime earnings. This, even as colleges battle falling revenue and declining enrollments with a business model at its “breaking point.” Universities that quickly ramped up digital infrastructure early in the pandemic now need a more measured, long-term digital transformation roadmap to help address the challenges brought on by COVID-19.
Any such roadmap should include artificial intelligence (AI), which has the potential to revolutionize higher education, with advancements offering enormous potential to tackle hard problems that intensified during the pandemic. But knowing the value of AI and adopting AI are two very different things—and for most universities, the challenge of bridging that gap remains.
Higher ed stakeholders are generally reluctant to adopt new technologies before they have been sufficiently de-risked elsewhere, and are especially reluctant to do so when resources are limited. It’s one thing for AI to recommend the next item on your online shopping cart. It’s another for technology to recommend an online curriculum that can directly impact a student’s ability to find or do a job.
What’s clear is the institutions that succeed in integrating AI into their transformation roadmap will have a distinct advantage over their competitors, which will translate into higher enrollments, higher revenues, and higher rates of student achievement.
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