Visit Modern Campus

Shift or Shrink: Why Artificial Intelligence Will Forever Change Higher Education and a Roadmap to Thrive in a Cloud-Filled Sky

Schools are having to adapt to a changing world, just as we all did, whether that be different in person rules, moving to virtual teaching, or even AI in the class. The ones that can’t keep up are going to be left behind.
Schools are having to adapt to a changing world, just as we all did, whether that be different in person rules, moving to virtual teaching, or even AI. Those that can’t keep up are going to be left behind.

Over the last year, I have had the pleasure of speaking with Terry O’Banion. If you are not familiar with his work, you should absolutely familiarize yourself with it. His construct of essential education led me to a line of thought I will try to articulate here.

Essential education encompasses a significant shift in how we address some assumptions core to our industry. O’Banion speaks of a need for a “brave leap” toward creating a new core curriculum grounded in four key skills. This call for a merger of liberal and workforce education outcomes is one of the systemic calls to action in higher education, as we adjust to our eventual post-pandemic world. Our key assumption of the role of higher education itself is somewhat grounded in the collective size of our institutions and their presence in our communities. Scale has long been an advantage—or has it?  

The passenger pigeon filled the skies of North America in the late 19th and early 20th centuries. Skies darkened on their arrival, and the sounds they made could be mistaken for a thunderstorm miles away. Within the span of one human lifetime, their numbers went from roughly 3 billion to zero. Explosive growth cycles, little genetic diversity, loss of habitat and hunting wiped them out. The birds could not shift their behaviour fast enough, and the world closed in around them.

Higher education has a lot to learn from this extinction. Like the passenger pigeon, our institutions of higher education are designed to thrive in complex ecosystems. Some of our institutions choose an elite model where enrollment sizes are small and target specific audiences. Others depend on larger enrollments, existing amidst cycles impacted by economics, and rely on federal financial aid. Unlike the passenger pigeon, our flock sizes intentionally vary. However, if we extend this analogy to higher education’s true mission—teaching and learning—we may be headed for a similar fate. Most colleges and universities are still designed around classes. This fundamental approach is more an economy of scale than anything. By grouping individuals together, we can more efficiently achieve the targeted learning outcomes across general education and almost every academic program. Based on this assumption, our financial models, faculty load and compensation and every aspect of our systems is preceded by the assumption we must predominantly group them together. While cohorts have been proven of value for a select type of desired outcomes, personalized learning remains rare in comparison.

This is far from the only approach or core assumption in education.  

Looking down from the lofty skies of higher education, we can see a new landscape emerging. The new apex predator expanding across business and industry is artificial intelligence (AI). The way we shop, the music we listen to, when and where we travel and even how we get our food have significantly changed thanks to AI and deep analytics. We live in a world of convenience, with predictive algorithms largely stored within the Cloud that make our lives easier. The human experience is forever changed by the arrival of AI. Higher education isn’t any different.

As our learning management systems (LMS) combine with our learning object repositories (LOR), a new layer of highly individualized deep analytics drives the right object to the right learner at the right time. AI sits behind these systems, leveraging machine learning as these systems adapt to individual learner preferences and readiness. What results is what I call ultra-personalized learning or UP! This is the relentless drive of AI across every profession and occupation. The grouping of learning outcomes remains grounded in taxonomies of sound curriculum design with a distinct difference. Classes as we know them have begun to dramatically shift. It’s no longer about a schedule of convenience to economies of scale but rather a focus on individual learner success. Normative distribution of achievement, common assessments, forced pacing and even biased entire curricula begin to fade. Look closely at Google, Amazon or any major textbook publisher, and you will see their parallel efforts to move all education in this ultra-personalized model. At their best, most colleges remain 35% successful in degree attainment and universities who preselect students average 60%. Ultra-personalization makes no apologies in seeking 100%. The flock as we know it is disappearing.

Shift or shrink is the choice that all leaders, faculty, staff and stakeholders in higher education face today. Continuing to push traditional college class schedules—whether remote, flex, hybrid or other—is a failed strategy like that of the passenger pigeon. This flock mentality cannot thrive in a world where the individual supersedes the flock. The good news is that a clear way forward exists for our institutions. As we increase levels of engagement between faculty and students, personalization rises.

Partnerships with AI companies, content providers and advanced technologies continue to advance and evolve. Emerging models embrace this highway to personalization and take advantage of the Cloud. This will shift the role of faculty, support and stakeholders across all our institutions. The journey could rely on these elements:

1)      Increasing the level of engagement across all courses, leveraging AI to assist with the burden of assessment where appropriate. Redesigning current courses and activating learning objects to use the power of predictive analytics, driving the right object to the right learner at the right time.

2)     Reimagining scholarships by incentivizing and supporting faculty, recognizing that they cannot possibly personalize all learning outside a long and sustained journey of activated collegiality. In this new ecosystem, faculty roles could shift with unprecedented levels of autonomy thriving across accountability metrics that foster scholarship and individual student success. 

3)      Shift from class schedule cycles to ultra-personalized learning where curriculums remain grounded in faculty and industry driven learning outcomes. The collective learning experiences may necessitate or even dictate groupings of individuals but only where appropriate. Ultra-personalization drives scheduling across paced—not open entry or open exit—milestones outside traditional frameworks emphasizing learner success.

Recognizing that this incremental shift will take time, we must not fall prey to the forces that impeded our evolution; some will not take kindly to ultra-personalization. Their arguments of academic integrity and personal fortitude will fall upon the rocks as technology and AI progress, whether our institutions follow or not. In over 30 years of leadership in higher and public education training thousands of teachers and faculty, I have not run into one person who refused to listen and collaborate when treated as a scholar. Even the most traditional stalwart, when invited into the conversation and genuinely empowered, will admit our skies are changing and we must work together as colleagues in our response. This is what I refer to as activated collegiality, where leaders, faculty and stakeholders work together to map out the next iterations of our journey. Begin at the course-level, and empower and support your faculty in migrating from our current flock mentality to sustainable models driven by individual success. 

In the coming decades, faculty will increasingly leverage technology and AI, forever changing their role from primary content delivery to designing and curating experiences. Whether in a university or college, they will continue their role as scholars in their fields as well as scholars of learning. AI will reduce their burden and optimize individual learner success as stakeholders continue to collaborate with us in building an unprecedented quality of life for everyone. This utopian vision must be pursued through transparency and trust. In the end, people are loyal to culture, not to strategy. If we wish to survive, we cannot follow the passenger pigeonhoping our flocks will adapt.We must shift our assumptions about classes, schedules and faculty load without diluting our commitment to integrity and engaging our communities and stakeholders. We must listen closely to the Terry O’Banions of the present and challenge the curricula that also drive our systems. A roadmap to this new and prevailing model begins with increasing engagement among our current courses and leveraging AI across our learning objects. We cannot navigate such skies in the absence of our faculty engagement while simultaneously shifting our assumptions on scholarship. From 3 billion to none within a lifetime offers us a profound lesson.

­­­­Shift or shrink.