The future of learning is about self-simulation
How we got here: a (very) brief history of learning in higher education
A large part of this answer is that they were created to serve another era—not ours. Yes, they originated in the medieval period but were reimagined during the Enlightenment to serve an industrial society—and remarkably this remains more or less the system of higher education we have inherited today. Not surprisingly, they were created with the ideals of that era: efficiency, hierarchy, specialization, and the compartmentalization of knowledge.
Learning in that era was about absorbing stable stocks of knowledge, having that learning certified with a degree, and then over the arc of your career deploying that learning in relatively stable and specialized professions. This, by the way, is the world of fixed majors leading to siloed professions. Want to be an attorney? Press the prelaw button when you enter college, then pull the law school lever after you graduate, and do corporate law for 40-50 years.
If you were a student in this era it was pretty easy to imagine your future: major in x and become an x—probably forever.And even if you were one of the elite few majoring in the liberal arts it was relatively easy to imagine how your core skills in, let’s say English, might map to a well-established adjacent profession in, let’s say, script writing.
Canal learning versus riding the rapids
In this world, the student’s journey through knowledge resembled a canal: a slow, stable, linear path from point a to point b. Every year (freshman, sophomore, etc.) the learner encountered the canal locks along the journey that artificially raised them up to the next level—and the journey continued the next year.
But here’s the problem: today the controlled world of canal learning makes no sense. The stable knowledge stocks that gave rise to most majors (and I am obviously focusing here primarily on professional majors) have given way to shifting currents of knowledge that interweave, eddy and ripple into and across each other. Like an ancient river that coils back on itself, these currents sometimes trick us by pinching off reservoirs of professional knowledge that appear fixed for a time but will inevitably evaporate. All that appears solid eventually melts into air.
Learning to simulate our future selves
In this post-industrial world of learning, helping students imagine their futures is a much more complicated thing—and yet it should be one of the core functions driving learning. Self-simulation,the ability of students to constantly and richly imagine the futures they might inhabit, and their impacts on those futures, should be a core purpose of learning. To be clear, self-simulation is not just an avenue toward self-actualization: how a student might be more or less fulfilled by pursuing different learning pathways. That’s the least interesting part of the equation.
Self-simulation is about value exchanges
The deeper purpose of self-simulation is to teach students to examine what they know, understand what they don’t know—and identify what they need to know to have impact on the world around them.So self-simulation is really about helping learners imagine and reimagine the possible ways they might exchange economic, social, or cultural value with the collective world around them that is in constant flux. And by the way, the ability to continually reimagine value exchanges between you and the world around you uses the same metacognitive skills that drive economic, artistic, and social innovation across our economy and society.
As I mentioned above, the problem is that universities are still organized with all of these early 20thCentury ideas that prevent them from bringing self-simulation into the center of the university experience—or even framing it as an issue. In many ways the entire framework of the modern university would have to be dismantled, including the ways knowledge is organized and accredited and the idea of a bounded period of learning followed by graduation into the “real” world.
A final thought about self-simulation and convergence
First, I’m not a technological determinist. I don’t believe technology alone determines the ways our world develops—or that it can solve all problems. But I’d like to end this piece by making a simple point. The urgent need to teach self-simulation to students across disciplines and throughout their university experience occurs as data processed by machine intelligence is contributing to the reshaping of our world: merging and morphing entire areas of our economy and society, eroding the barriers to accessing information and making sense of it, helping us predict and simulate things we could have never imagined 25 years ago. These convergencetrends will powerfully contribute to remaking higher education, but they will also help students imagine and simulate how what they learn could impact they ways they exchange value with a world that is in constant flux.
What if, for example, micro-credentials certifying learning experiences were machine learning agents that could detect how students flowed through them—where they came from and where they went? Looking at this from a career perspective, imagine a vast network of these intelligent credentials detecting massive student flows across multiple universities and at the same time incorporating the remarkably nuanced data from job trend forecasting companies like Burning Glass. This isn’t science fiction; it could be done at scale right now. In this world of convergent learning, a student could create their digital twin, simulating with great fidelity how different sets of learning experiences might lead to certain professional outcomes. A set of credentials could “say” to a student for example, “move through me and you could end up in a professional experience at the intersection of sectors x and y. There is no job category yet for this work but people in these roles report being fulfilled and challenged with high rates of learning.”
To be clear, self-simulation isn’t just about technology—it’s as much about things like pedagogy, the organization of knowledge at universities, rethinking and the discrete, time-bounded nature of the student experience, and integrating learning from places and sources far beyond the university campus. But the ways data contributes to the relentless reshaping of our world will both make self-simulation imperative for students to master—and offer them ways to make this process richer and more intimately connected to their unique lived experiences.