Who benefits from AI productivity gains? It all depends on metrics, not just technology

This post originally appeared on the Christensen Institute blog and is republished here with permission.

Key Point:

At the Christensen Institute, we have long argued that when a technological innovation emerges, the path it takes has little to do with the technology itself, but rather with the model that surrounds it.

This distinction is crucial in understanding the new avenues that Generative AI (GenAI) brings to schools and higher education institutions, especially when it comes to the dramatic efficiencies that AI can enable.

GenAI is already having a significant impact on expanding human productivity. In other service industries, such as consulting, researchers have found that AI can increase the productivity of skilled workers by up to 40%. Early estimates suggest that teachers could use AI to save 20-30% of the time they currently spend on administrative tasks.

So while there are nearly limitless possibilities for how AI will impact what and how students learn and their creative pursuits, in the short term much of AI will be focused on making existing systems more productive.

Efficiency gains are a welcome development in a resource-constrained education system. But not all efficiency gains are created equal. Organizational norms and policy incentives determine how newly gained time and resources are reallocated and absorbed back into the system. Institutions, educators, and students can each win and lose in dramatically different ways.

As the market begins to chase the many efficiencies that Gen AI promises, there are three trends to watch.

1. Educator Competence: Do you give connectors time and breathing space?

Whether teachers spend up to 40% of their time planning lessons and keeping track of students, or counselors spend more than a third of their time scheduling courses and testing academic achievement, Gen AI has clear potential to free up valuable teacher and staff time.

There is serious excitement among AI evangelists that educators will pour their newfound time into connecting with students. Unfortunately, as appealing as this proposal may be, it is based on the false premise that schools are designed to optimize connection in the first place. Most educators wholeheartedly agree that relationships matter, but schools rarely regularly or rigorously measure students' connections with educators, peers, or community members.

So what happens with the freed-up time? It could start with making the work of educators and advisors much more sustainable. This would be a very good thing. Most educators work mediocre salaries with huge caseloads, with many working unpaid overtime and sometimes working second jobs to make ends meet. If AI could offset some of that burden, it could reduce burnout, increase retention, and make teaching a more attractive profession to pursue in the first place.

Early data suggests that this is already happening among frequent AI users: Aaron Cuny of AI for Equity collected data from staff at six charter management organizations across the country. The data showed that 84% of those who use AI daily or weekly said that AI has made them more positive about their jobs in the continuing education sector (compared to 52% of respondents overall).

Bottom line: Gen AI has the potential to make educators' work sustainable, but without new priorities and metrics, expected benefits like building connections are unlikely to be realized at scale.

2. Student support: Fixing or preserving a broken system?

AI is also starting to bring efficiencies to the wilderness of student support, especially in higher education, where universities are scrambling to support the up to 40% of students who drop out before completing their degree because they haven't paid off their tuition.

The barriers to graduation mirror the complexity of the higher education system itself. From keeping scholarship paperwork up to date to securing housing to registering for classes, AI-enabled chatbots streamline the incredibly complicated system of checklists and departments that students must navigate just to get by in college. In other words, AI offers an appealing workaround in a system that's far from student-centric.

The clearest example predates generative AI and is seen in universities deploying text-based chatbots to help students stay enrolled. Georgia State University’s partnership with MainStay has been well-received, and some of these models have seen double-digit increases in student enrollment and retention.

GSU is an exemplar in this field, not only because it has withstood gold-standard RCT studies, but also because of the institution's commitment to focusing on student success, not just the bottom line. Case in point: a portion of revenue from student retention was reinvested to hire more advisors, not fewer. In other words, what looks like pure efficiency gains are actually driving deeper investments in student support structures.

I doubt that GSU's approach will work at other universities, especially those with financial difficulties, which raises a larger question: Are AI-powered student-support bots subsidizing higher education's broken business model, or are they helping universities change the system to become more student-centric?

Bottom line: The most promising AI-enabled student support models will use technology to better understand how to make operations more efficient and make changes that significantly reduce navigation hurdles. However, if AI is adopted as a pure efficiency innovation in traditional systems, we are unlikely to see a shift in the underlying structures that make college graduation a gamble.

3. Social connections: Cut costs or lose connections?

Making education more sustainable and college more accessible are noble pursuits, and while it may not completely overhaul the education system, the efficiencies that AI brings could make the system work better for more faculty and students.

But as AI becomes part of the operating system for education and applications become more prevalent, especially for families and students, we need to think bigger. If an AI tool meets the current standards of the market, it risks becoming irrelevant amid the shuffle.

The education market is primarily focused on learning and achievement metrics, not tools that build relationships and prosocial behaviors. This means that the more common AI companions, coaches, and anthropomorphic bots become in learning and support models, the weaker students' social connections may become. As a result, social networks that lead to long-term support and professional opportunities may disappear.

As Gen AI becomes more sophisticated and personalized, we begin to walk a tightrope between increased productivity and potential loss of connection. This raises questions on the minds of many leaders in education and AI: When will your AI companion be a helpful co-pilot, and when will it take away time spent building genuine connections that help you achieve your goals? When will your AI companion be a caring assistant that expands human potential, and when will it erode empathy? When will your AI companion be a highly personalized coach that democratizes support, and when will it reduce the number of people who know you and are willing to bet on you?

Bottom line: The threat that AI poses to student connections won't happen overnight, but in the long term, when productivity is at the core of most policies and revenue models guiding education, sacrificing human connection will become the cost of doing business.

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

I’ve described these possible futures as either/or. Many readers will be hopeful that with the right tools and policies in place, AI can offer both avenues to free up educators’ time and deepen connections, amending and ultimately transforming current systems, unlocking individual productivity, and fostering diverse connections.

I admire that optimism, but we must remember that an entirely new set of student-centric metrics must emerge to guide that growth. In investment terms, we need a marketplace portfolio approach — investing in tools that appeal to the existing system's incentives for efficiency while cultivating tools that aim for higher goals for schools and students.

Julia Freeland Fisher

Julia Freeland Fisher is director of education research at the Clayton Christensen Institute and author of Who You Know: Unlocking Innovations That Expand Students Networks.

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