Education Philanthropy in the Age of AI: 3 Key Questions to Future-Proof Your Philanthropic Strategy

Gartner’s recent update to their Hype Cycle for AI – an analysis that gauges the maturity and adoption rates of new technologies – placed generative AI at the peak of inflated expectations. This is a phase marked by a surge in enthusiasm and a flurry of headlines on innovative success stories. 

Our conversations with and surveys of education philanthropists corroborate Gartner’s assessment: In a recent Tyton Partners pulse survey, 65% of education philanthropists indicated being “curious” or “excited” about AI’s impact on their focus area. But as we navigate what AI researcher and economist, Avi Goldfarb, terms the “between times of AI,” it’s imperative for philanthropists to channel this excitement into meaningful inquiry. 

To streamline this exploration, we can focus on three guiding questions that can help reassess your philanthropic strategy in the AI era:

1. Goals: Is your strategy still oriented towards the most relevant student outcomes and objectives?  
2. Speed: Is your strategy able to address challenges in a timely manner?  
3. Flexibility: Is your strategy able to easily adapt as technology continues to disrupt the landscape? 

Shifting goal posts

The first way to approach this question is to consider how is AI changing the nature of your existing student outcome objectives. For example, early data shows that 35% of employees report that their work responsibilities have already changed due to AI tools. This has implications for any philanthropist working on career development or upskilling specifically, and education more broadly. As the world of work and education change, grantmakers need to reconsider what success means for learners and other stakeholders across multiple career-areas of focus.  

We should also consider how AI might shift the priority ordering among different outcomes – if your grantmaking strategy drives students to AI-threatened careers, you may no longer be setting them up for success in the long-term. Early analyses already predict, as did the World Economic Forum in its most recent jobs report, that AI could “lead to high churn – with 50% of organizations expecting it to create job growth and 25% expecting it to create job losses.” We should ensure that we are equipping students for an imminent future by setting them up in high-demand careers, not clinging to an obsolete past. 

The rate of change could demand faster time-to-impact

AI has been evolving at breakneck pace. At last year’s ASU-GSV, Bill Gates admitted to wildly underestimating the progress AI would undergo. This speed of change means that challenges faced by learners and other stakeholder groups could become more acute and sudden, meaning philanthropists may need to be prepared to move on important challenges faster. In select areas, the luxury of having the same set of problems persist for years, and having the time to carefully assess and plot long-term plans, could diminish. 

In Tyton’s own Time for Class 2023 data on AI, we saw “Preventing student cheating” jump to the top instructional challenge reported by instructors in 2023, up from the 10th in 2022. Tyton has measured instructional challenges for years, and this jump represents the fastest rate of change we have seen to date. Academic integrity went from a long-time back-burner issue, to immediately front and center in a matter of months thanks to ChatGPT’s introduction. As we have had conversations with instructors, they have consistently reiterated that these challenges are not ones that can be dwelled on – they need support as soon as possible. How this plays out has pressing implications for student impact, and different players have essentially had just months to go from initial outcry, to understanding the challenge, to trying to address it – this is an urgency only surpassed by the COVID closures. 

The ongoing academic integrity challenge we are currently facing is a clear demonstration of how quickly problems can shift when driven by the AI arms race. Philanthropists should closely consider if their existing strategy is addressing needs on the appropriate time horizon. 

Flexibility is a necessity

These changing objectives and speed of change, lead us to consider the adaptability of existing strategies. Building in optionality and adaptability to steer as the landscape shifts has moved from being a nice-to-have to a necessity.

The future impact of AI, and any other disruptive technology, remains foggy, and to remain adaptable, planning time horizons should both shrink and include more thoughtful scenario analysis. 

For example, early predictions suggest we could land in a number of very different places when it comes to AI’s impact on educational equity. Michael Trucano of The Brookings Institution proposes that AI might usher in a new kind of digital divide, one centered on access to human educators rather than just technology. Strategies centered on access will need to be nimble, ready to adjust to such unforeseen divides. The question then becomes: How can your strategy fluidly navigate these changes without necessitating a complete overhaul at every new challenge? 

Balance agility with composure

This is not a call to throw out your old playbook, but to examine it critically. While it is essential to be agile in this dynamic landscape, it’s equally crucial to maintain a sense of purpose and direction. Disruption is cyclical, and while it’s tempting to react to every new development, the key lies in parsing the reality of the disruption from the hype, to discern what warrants a strategic shift and what does not. The AI era beckons philanthropists to not just react, but to anticipate, stay flexible, and lead with vision.  

As you and your organization think through these challenges and opportunities, we are always happy to discuss. Reach out to have a conversation. 

In our coming posts, we will tackle how to consider risks to your current grantees, and how AI might change the way grant-making as a process is conducted.