Why we're betting more on teachers
- without abandoning students.
This past December, the Zhou & Eesley Family Foundation ran a program at the Penang Science Cluster in Malaysia. Roughly twenty-five teachers came in for training in AI literacy and design thinking. We taught for a few days. We left.
In the months that followed, those teachers — without further intervention from us — brought what they’d learned back into their classrooms, with the new curriculum integrated into their teaching. From the six teachers who responded to the post-training survey, those six alone directly reached 611 of their students. Extrapolating across the full cohort, the curriculum likely reached on the order of two thousand five hundred students.
That’s roughly 100 students reached per teacher trained. By every reasonable measure, this was the most measurably effective program our small foundation has ever run.
It is also the program that finally rebalanced how Lijie and I think about what philanthropic education work is for.
What we already knew, and weren’t acting on.
Lijie has been around teacher training for far longer than the Foundation has existed. Before her engineering career — before Silicon Valley, before us — she ran teacher-training programs in rural China and worked as a program manager at China’s Ministry of Education. She had spent years watching what good teacher PD could do at scale, and what poor teacher PD couldn’t do at any scale.
So when we co-founded the Foundation in 2021, she’d already been making the multiplier argument for a decade. I (Chuck) was the holdout — my instinct, coming from Stanford research, was to value direct contact: be in the room, see the students, capture the texture of the work in real time. We compromised by doing both, often inside the same program: our 2023 LOHADA visit in Tanzania trained roughly twenty teachers alongside the fifty-plus students we taught directly, and our work at Fulbright University Vietnam has consistently paired teacher-development sessions with the student-facing programming. But the bias tilted toward students in the room — that’s what we showed up to do, and what we celebrated. Penang is where the math finally became the headline.
What 100-to-1 looks like.
Penang is where Malaysia builds its semiconductors. Intel, AMD, Lam Research, Bosch — they all have their fingerprints on the island. It is a place that knows what high-skilled technical labor looks like and is short of it.
The Penang Science Cluster is a non-profit founded by industry leaders to build the pipeline. Their model is the right one: convene teachers from rural schools across the state, train them in disciplines the schools don’t know how to teach, and let those teachers go back and teach. We were brought in to contribute the AI and design thinking module — a small input into a much larger program PSC was already running well.
The module was one day of intensive work with the cohort of teachers, built around a curriculum we’d developed specifically for the way Malaysian middle schools actually work. (You’d be surprised how much philanthropic curriculum dies on contact with a school where the kids don’t have laptops and the teacher only has 35 minutes a week with each class.)
After the training, the teachers brought what they’d learned back to their classrooms. PSC ran a post-training evaluation, with six teachers and eleven students responding so far.
The numbers from that subset: 100% of responding teachers adopted at least one new AI tool or design thinking technique — most are using ChatGPT, with Gemini and rapid prototyping in the mix. Every responding teacher reported saving time through AI workflow integration — most one to two hours per week, the rest three to five. Of the eleven students surveyed, 73% reported discovering a career path they had not seriously considered before — software engineering, chemical engineering, product management, AI research, public policy, even chef — and the remaining 27% said the workshop gave them new specifics on a path they already liked. Teachers rated the workshop 8.7 out of 10 on average for “would you recommend a colleague”; students rated it 4.6 out of 5; and on a separate question, students rated their confidence in using AI to research and plan their futures at 4.1 out of 5. Survey responses are still coming in; these are the early numbers.
The numbers miss what’s actually interesting. A teacher, in their own words:
“Since the workshop, I have started integrating ChatGPT into my teaching workflow by using it to help design lesson plans, generate discussion questions, and create differentiated learning materials for students. It has also been useful for quickly drafting emails and administrative documents, which saves time and allows me to focus more on student engagement.”
A student:
“I have discovered to become an AI Researcher and Public Policy Researcher. What draws me to this career path is that it combines both my interests and my strengths… Before joining this workshop, I felt unsure and unclear about my future career direction. This workshop gave me the opportunity to explore my interests and skills more deeply, and helped me see how they can be applied in ways that are beneficial not only to myself, but also to society.”
Those are the easy numbers and their human counterparts. What I want to talk about is what they imply, and — equally — what they don’t.
The thing about the math.
If you spend $X to teach 50 kids directly for a week, the marginal cost per student-hour is high, the long-tail effect is approximately zero, and the work doesn’t accrete. The fiftieth kid is not measurably better off because you taught the first forty-nine.
If you spend roughly the same $X to train twenty-five teachers — who then teach roughly a hundred kids each per cohort, year over year — the cost-per-student-reached collapses, the long-tail is the dominant term, and the work compounds.
This is not a hard observation. It is something the larger foundations figured out decades ago. (The Gates Foundation’s K-12 work, when they were doing it, was almost entirely teacher-training oriented for exactly this reason.) But it is something small foundations are slow to internalize, because direct teaching has a kind of moral romance that teacher training does not.
When I tell people the Foundation reached thousands of students in Penang last year, the natural follow-up is: “Wow, were you there?” And the honest answer is: no. We trained roughly twenty-five teachers in December. Those teachers reached their students in February, in March, in April. We never met them.
That is not a failure mode. That is the point.
The honest limits.
I want to be specific about what this analysis is not claiming, because someone with serious training in education research will land most of these critiques the moment they read the numbers.
It is not claiming the 100-to-1 multiplier is generalizable. PSC is an unusually well-resourced partner: industry-funded by Intel, AMD, Lam Research, and Bosch; staffed by people who recruit teachers from rural schools across the state; equipped with rigorous evaluation infrastructure we did not have to build. The teachers we worked with were carefully selected — motivated, experienced, ready to adopt. Most teacher-training programs run with self-recruited teachers in contexts without that wraparound. The multiplier collapses.
It is not claiming teacher PD generally works. There is a long, sobering literature on teacher professional development — most one-shot training has near-zero effect on student outcomes. The mechanism that distinguishes the rare PD programs that do work, and Penang among them, is the wraparound: rigorous teacher selection, multi-day immersion, curriculum specific enough to use Monday morning, embedded measurement, and follow-up that turns one-shot training into multi-year relationship. Teacher training without that wraparound is direct teaching in a different costume — fewer kids reached, same lack of compounding.
It is not claiming we did all the work. PSC did the teacher recruitment, the venue, the school relationships, the follow-up coaching, and the measurement that produced every number I’m citing. We provided the AI and design thinking module. The multiplier belongs as much to them as to us. Probably more.
And it is not claiming we measured what actually matters most. A “would you recommend this” rating measures satisfaction; “discovered new career paths” measures self-reported insight; “adopted at least one new technique” measures observable behavior. None are what a serious education researcher would call a learning outcome. A six-month follow-up showing students retained AI concepts they didn’t have before, or that the trained teachers’ classes outperform peers on independent assessment — that would be rigorous measurement. We don’t have that yet. We’re building toward it for the next cohort.
What we are claiming, more modestly: with the right partner and the right wraparound, in one program in one country in one year, we produced an order-of-magnitude better cost-per-student-reached than direct teaching, and the early indicators are unusually strong. Enough to shift the bias of our 2026 programming. Not enough to recommend every small foundation drop direct teaching tomorrow.
Why we still teach directly.
We have not stopped teaching directly. We will not stop. Three reasons matter.
The first is signal acquisition. You learn things in a classroom you cannot learn from a teacher’s after-action report or a survey instrument. When Lijie taught at LOHADA in Tanzania, what she came back with was not a number — it was an intuition for which parts of our entrepreneurship curriculum survived contact with East African secondary students and which parts collapsed. That intuition then went into the teacher-training material we now run elsewhere, including the teacher cohort at LOHADA itself. Direct teaching is the R&D function of a teacher-training operation.
The second is relationship. The Foundation’s most durable partnerships — Tanzania through Chuck’s former student James Juma, Vietnam through his former student Bao Phan, MIT MEET through their leadership — came from being physically present early on. The people who later open doors for us, who invite us back, who introduce us to other partners, did so because they met us in a room with students. We will keep showing up in rooms with students.
The third is that we love it. This may sound trivial in a piece otherwise devoted to math. It isn’t. There is something about teaching a class of kids in Molokai or Kampala that we don’t get from any other part of our lives. Optimizing the Foundation entirely for measurable multiplier effects would optimize out the part of the work that gives us the energy to do the rest of it. That is a bad trade.
The rebalancing is exactly that — a rebalancing, not a replacement. Most new programming dollars now bias toward teacher-anchored models. Some fraction continues to fund direct teaching, treated explicitly as research, relationship-building, and the part of the work that keeps us human.
Stepping back.
I’ve been lucky to spend much of my career teaching: Stanford undergraduates, doctoral students, corporate executives in dozens of countries, engineers at companies that don’t usually let outsiders in. The K–12 and refugee work the Foundation does is one slice of that. It’s worth saying out loud why teaching in these settings matters — to the people in the room, and to us.
For the people in the room: most of what’s worth knowing about how innovation, entrepreneurship, and AI actually play out doesn’t make it into textbooks. The frameworks that work, the failure modes you’d never anticipate, the way institutional context changes everything — these get transmitted person-to-person, classroom by classroom. Students who never get into a room with someone who’s spent twenty years thinking about how this stuff works are not less talented. They are under-resourced. Teaching is one of the few interventions that closes that gap directly.
For us: teaching is where the research becomes legible. I leave a classroom of Korean executives, or Vietnamese engineers, or a Penang teacher cohort, with sharper questions than I came in with. The doctoral student who pushes back on a framework, the executive who tells you why your model breaks in their industry, the teacher who explains which part of the curriculum students actually struggle with — these are inputs no peer-review process produces. From a research perspective, the Foundation’s K–12 teaching, our work with refugee entrepreneurs in Uganda, and the corporate teaching and the Stanford classroom are the same activity: they are the source of the next paper, the next program, the next correction to what I thought I knew.
Teaching is not what you do once you’ve stopped learning. It is one of the better ways to keep learning. The Foundation exists, in part, because we want our work to keep being shaped by what we hear from the people we teach.
Why we work in partnership.
The honest version of “we trained twenty-five teachers” is “we contributed an AI and design thinking module to a teacher-training program built and run by the Penang Science Cluster.” We didn’t invent the multiplier model. We didn’t recruit the teachers. We didn’t build the relationships with the rural schools, or the evaluation infrastructure, or the years-deep credibility with industry partners that makes PSC the convener it is. We were the new collaborator at a table other people had set.
This is not a complaint. It is the model.
A small foundation cannot — and should not try to — replicate what a serious operator like PSC has built. Their staff have been at this far longer than we have. They have institutional relationships, regional credibility, and an evaluation discipline we are still building toward. We were inspired by what they’ve put together, learned from how they run it, and contributed the specific thing we could contribute well — AI and entrepreneurship curriculum built on Stanford research, plus the Stanford brand and connections that opened doors PSC could then walk through. Partner brings the structural thing. We bring the specific input. That is the only sensible model for a foundation our size.
This is not unique to Penang. Every Foundation program is built around a partner who knows their context better than we ever will: MIT MEET in Jerusalem, LOHADA in Tanzania (anchored by James Juma, where the work pairs teacher training with direct student teaching), Fulbright University Vietnam (the same combined model), Makerere University Business School and Challenges Uganda for the refugee entrepreneurship work in Kampala, Kaunakakai Elementaryon Molokai through teacher Kawika Gonzales, ITRI in Hsinchu, the SFSU CS department before its alumni network took over the work entirely. We bring what we have. They bring what they have. The work compounds because of the partnership, not despite it.
Without the partner, the multiplier is a hypothesis. With them, it’s the thing.
What this means if you run a small foundation.
A few practical observations from the last year:
1. Teacher training without curriculum is a waste. The teachers we worked with in Penang were motivated and ready to adopt. What they needed wasn’t inspiration. It was material — a curriculum specific enough to use on Monday morning and flexible enough to fit their actual classroom constraints. Most of the philanthropic AI-literacy material out there does not survive that test. Building good curriculum is the most underrated thing a small foundation can fund.
2. Multipliers compound only with continuity. A one-shot teacher-training program with no follow-up is direct teaching in a different costume. The foundations getting real multiplier effects are the ones building multi-year relationships with the same partners — annual cohorts, refreshed materials, alumni teachers becoming mentors for the next cohort.
3. Track the kids you don’t meet, and measure what they actually learned. If your evaluation framework only counts students you taught directly, you’ll inadvertently optimize away from teacher training. But also: if your framework only counts recommendation ratings and “adopted at least one tool,” you’ll mistake satisfaction for learning. Build both behavior and outcome metrics into the partnership upfront.
4. Direct teaching is your R&D budget; account for it that way. If you cut direct teaching to zero, you’ll be running a teacher-training operation with stale material and no relationship pipeline within three years. Treat your direct-teaching work explicitly as research and relationship-building, fund it accordingly, and don’t apologize for the lower headline numbers.
5. Find your operating partners and let them lead. A small foundation that tries to be the operator will underperform a small foundation that finds excellent operators and contributes the specific input those operators don’t already have. We did not build PSC. We brought a curriculum module to a model they had spent years developing. If you are sizing up a new program and you’d be the operator, be honest about whether you should be — and if there’s a serious operator already in that geography, ask whether you’d do more good as their collaborator than as their parallel.
Where this leaves us.
The Penang program will run again this year, with more teachers, stronger curriculum, deeper follow-on, and outcome-based measurement we should have built in the first time. We are looking at similar teacher-anchored, partner-led models for our work in rural Hsinchu and at Kaunakakai Elementary on Molokai. We continue to do direct teaching — usually paired with teacher-training cohorts in the same program — at LOHADA in Tanzania, at Fulbright University Vietnam, with refugee entrepreneurs in Uganda, and with university students wherever the cohort itself is the multiplier. Both because it works for those audiences, and because it keeps the rest of the operation honest.
There is a version of this story that ends with: “and so we figured it out, and now we know how to do philanthropy at scale.” That is not the story I am telling. We figured out one thing about one program in one place, in close collaboration with a partner who’d been figuring it out for years before we showed up. We have eight programs in eight places, and the model that worked in Penang may or may not transfer to a refugee settlement in Uganda or a rural school on Molokai. We are betting that a version of it will.
Small foundations like ours often spend our first few years confused about whether we are a teaching organization, a funding organization, or an operating organization. We are not, mostly, any of those alone. We are an organization that finds excellent partners, equips them with what they need that they don’t already have, contributes what we can do well, and keeps a small, deliberate, joyful slice of the direct work for ourselves — for reasons that have less to do with measurable impact than with knowing what the work actually feels like.
Twenty-five teachers reached on the order of two thousand five hundred kids without us. They reached them through a program PSC built. With curriculum we contributed. That partnership is not despite our smallness. That partnership is what makes our smallness work.
— Chuck