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As the market starts to chase the many efficiencies Gen AI promises, here are three trends to watch for education's future with AI.

Metrics, not just technology, will determine who gains from AI’s productivity gains


As the market starts to chase the many efficiencies Gen AI promises, here are three trends to watch

This post originally appeared on the Christensen Institute’s blog and is reposted here with permission.

Key points:

At the Christensen Institute, we’ve long argued that when a technological innovation emerges, the path it follows is rarely about the technology itself, but about the model wrapped around the technology.

That distinction will be critical to understanding the new paths Generative AI (GenAI) stands to blaze across our schools and postsecondary institutions, particularly when it comes to the dramatic efficiencies AI stands to unlock. 

GenAI is already showing immense impact on expanding human productivity. In other service industries, like consulting, researchers have found AI can boost skilled workers’ productivity up to 40%. Some early estimates wager that teachers using AI could save 20 to 30% of their time currently spent on administrative tasks. 

That means that while there are near-infinite possibilities of how AI will shape what and how students learn and their creative pursuits, in the near term, much of AI will be about making the existing system more productive. 

Efficiency gains are welcome advancements in an education system rife with resource constraints. But not all efficiency gains are created equal. Organizational norms and policy incentives will drive how newfound time and resources get reallocated and absorbed back into the system. Institutions, educators, and students each stand to win–and potentially lose–in markedly different ways.

As the market starts to chase the many efficiencies Gen AI promises, here are three trends to watch:

1. Educator capacity: Freeing up time to connect…or giving space to breathe? 

Whether it’s teachers spending up to 40% of their time lesson planning and maintaining student records or some counselors spending over a third of their time on course scheduling and academic testing, Gen AI has clear potential to free up precious educator and staff time.

There’s earnest hope among AI evangelists that educators’ newfound time will be poured into connecting with their students. Unfortunately, appealing as the proposition may be, it rests on the faulty assumption that schools are designed to optimize for connection in the first place. While most educators would wholeheartedly agree that relationships matter, schools rarely measure students’ connections—with educators, peers, or community members—with regularity or rigor. 

So, what might happen to time freed up? It may begin by making educators’ and advisors’ jobs much more sustainable. That’s a very good thing. Most educators operate with daunting caseloads and middling pay, leaving many educators working unpaid overtime and sometimes taking on second jobs to make ends meet. If AI can pick up some of that slack, it could mitigate burnout, boost retention, and make teaching a more attractive profession to pursue in the first place.

Early data suggests that’s already happening among frequent AI users. Aaron Cuny of AI for Equity has collected data from staff at six nationwide charter management organizations. The data shows that an impressive 84% of those who were daily or weekly AI users were “more excited about continuing education sector work because of AI” (as compared to 52% of all respondents).

The takeaway: Gen AI has the potential to make educators’ jobs sustainable; but without new priorities and metrics, hoped for upsides like building connections are unlikely to unfold at scale.

2. Student support: Fixing broken systems…or upholding them?

AI is also starting to lend efficiencies to the Wild West of “student support.” That’s especially true in higher education, where colleges are scrambling to support the up to 40% of students who drop out—taking their tuition dollars with them—before earning a degree. 

Barriers to college completion mirror the complexity of the higher education system itself. Whether it’s staying up-to-date on financial aid paperwork, securing housing, or registering for classes, AI-enabled chatbots are streamlining the punishingly complex system of checklists and departments students have to navigate to stay afloat in college. In other words, AI offers a compelling workaround in a system that is far from student-centered.

The clearest example of this predates Generative AI, with colleges enlisting text-based chatbots to support college persistence. Some of these models, like Georgia State University’s much-lauded partnership with Mainstay, have posted double-digit gains in student enrollment and persistence.

GSU is an exemplar in this space, not only because it stood up to gold-standard RCT research, but also because of the organization’s commitment to doubling down on student success, not just revenue. Case in point: a portion of the revenue gained from retaining students has been poured back into hiring more, not fewer, advisors. In other words, what could look like pure-play efficiency is actually driving deeper investment in student support structures. 

I suspect other campuses, especially those in financial straits, may not share GSU’s calculus. That begs a larger question: are AI-enabled student support bots subsidizing a broken business model of higher education or helping colleges bend their systems to be more student-centered? 

The takeaway: The most promising AI-enabled student support models will use technology to better understand how to streamline their enterprise and then make changes to drastically ease navigation hurdles. But if AI is enlisted as a pure-play efficiency innovation in the traditional system, we’re unlikely to see shifts in the underlying structures that make college completion a gamble.

3. Social connectedness: Cutting costs… or costing us connections?

Making teaching more sustainable or colleges more navigable are noble pursuits. While it may not spell whole-cloth reinvention of our education system, the efficiencies AI offers could make the system work far better for far more staff and students alike.

That said, there’s a bigger-picture consideration as AI becomes part of education’s operating system, particularly with family- and student-facing applications. If it supports the market’s current AI tool metrics, relationships are vulnerable to getting lost in that mix. 

Because the education market focuses mostly on metrics around learning and attainment, it doesn’t tend to demand tools that build relationships and pro-social behaviors. That means the more commonplace that AI companions, coaches, and anthropomorphized bots in learning and support models are, the more fragile students’ social connectedness may become. In turn, social networks that lead to long-lasting support and professional opportunities could vanish.  

As Gen AI becomes more sophisticated and “personalized,” we’re going to start walking a tightrope between productivity gained and potential connection lost. That begs questions I hear few leaders in education and AI circles more generally asking: When is an AI companion a helpful “copilot” and when is it chipping away at your time spent building authentic connections that support your goals ? When is it an obliging “assistant” expanding human potential and when is it eroding your capacity for empathy? When is it a highly personalized “coach” democratizing support and when is it shrinking the number of people who know and are willing to take a bet on you?

The takeaway: The threats AI poses to student connection aren’t going to appear overnight. But in the long term, if productivity is at the core of most policies and revenue models that guide education, sacrificing human connection will become the cost of doing business. 

Conversations about AI and success metrics need to go hand-in-hand

I described these possible futures as either-or. Many readers probably hope that with the right tools and policies in place, AI can offer a both-and path – both freeing up educator time and deepening connections; both fixing the current system and, ultimately, transforming it; both unlocking individual productivity and fostering diverse connections. 

While I admire that optimism, let’s not forget that a whole new set of student-centered metrics will need to emerge to guide that growth. In the language of investing, we will need to see a portfolio approach in the market, investing in tools that appeal to the existing systems’ incentives to pursue efficiency while incubating tools that aim for our higher ambitions for schools and students. 

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