Two women crouch among trays of seedlings in a plant nursery.

2026 Goalkeepers Report

AI, equity, and the Choice We Can’t Delay

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The 2026 Reportat a glance

The choice is being made now.

AI could be a great equalizer—or widen the gap. This is not a long-range prediction. It's a present-tense choice.

Read the introduction

Left to the market, the rich get AI first

Left to the market, AI will be designed by and for the richest people in the world.

Explore the data

Built for frontline workers, it can work for everyone.

Making AI useful for the people and places where it could make the biggest difference is not an evolutionary leap. It's within reach.

Read call to action

Nothing hasfelt like this

AI could be a great equalizer—or widen the gap. This is not a long-range prediction. It's a present-tense choice.

The phone in a health worker’s hand could help her recognize a dangerous pattern, know when to refer a patient, and anticipate which medicines her patients will need before they run out. It could connect what she sees on an individual level with the total knowledge of the wider health system. And it could help her bring better care to more people, even in places that are hard to reach.

Healthcare is only one example of how AI could improve lives. I believe AI could also have a massive positive influence on the education and economic opportunities available to people all over the world, regardless of whether they live in a wealthy district or low-income community.

©Gates Archive/Jean Bizimana, Rwanda

My whole life, I’ve been focused on how innovation can expand opportunity.

I remember the start of the personal computing revolution: a time when technology became more powerful and affordable, opening up new ways to improve lives around the world.

I’ve lived through waves of change that felt, in the moment, like the most significant thing happening on earth.

Nothing has felt like this.

©Gates Archive/Saumya Khandelwal, India

AI is different: a technology moving faster, reaching more people than anything before it. Because of that, no one knows exactly what the future will look like even five or ten years from now, including me.

Some people have never been more worried. Others have never been so excited. I understand where both are coming from.

I’ve written separately about the risks that AI poses—to jobs, to security, and to our institutions. Those risks are real and deserve serious attention.

Here, I want to focus on something that often gets lost: the possibility that AI, if shaped well, could expand who has access to solutions, opportunities, and knowledge that has too often been out of reach.

AI could be a great equalizer—or widen the gap. This is not a long-range prediction. It’s a present-tense choice. Here’s what’s at stake.

AI is different: a technology moving faster, reaching more people, than anything before it.

Baby Jakes undergoes a health checkup by Vyonne Njeri, a clinical officer at Penda Medical Centre, as his mother, Sylvia Mayabilo, accompanies him at the facility in Zimmerman, Nairobi.

Where theinequitiespersist

We’ve made extraordinary progress. But on current trends, the gaps between rich and poor don’t close—they persist for decades.

©Gates Archive/Khaula Jamil, Pakistan

We’ve made progress. But if current trends persist, so does inequality.

The last 25 years have shown what is possible when innovation is deliberately put to work for the people who need it most. Since the start of the 21st century, child deaths have been cut in half. Extreme poverty has declined. Deaths from HIV, tuberculosis, and malaria have fallen significantly.

These gains did not happen by accident. They happened because the world invested in innovators, frontline heroes, and tools that save lives—and made sure they reached the people who needed them.

But that progress is fragile. In some places, it has already slowed, made worse by devastating cuts to funding for global health in recent years. Even still, by 2045, the world should be, on average, better educated, healthier, and less poor than it is today.

But averages hide the full story.

New data from the Institute for Health Metrics and Evaluation at the University of Washington shows health and opportunity gaps between the richest and poorest people will stay stubbornly wide unless something changes dramatically.

For example, in 2045, a young adult in sub-Saharan Africa is projected to still have three fewer years of schooling than their peers in high-income countries. That’s big progress from where things stood 25 years ago—but it’s still a gap that shouldn’t exist.

Nearly 900 million people will still be living in extreme poverty—low-income countries will have 330 times more people living in extreme poverty than high-income countries.

And more than 3 million children will still die each year from diseases we already know how to prevent today.

The Schooling gap won’t close itself

Years of Schooling

high-income vs. low-income countries, 2000–2045

Line chart: average years of schooling from 2000 to 2045, projected after 2025. Low-income countries rise from about 4 to 10 years; high-income countries stay near 12 to 14.

Still 3 years behind in 2045

11

years

of education in sub-Saharan Africa

14

years

of education in high-income countries

Poverty is shifting, not shrinking

People living in extreme poverty by region

2000–2045 (millions)

Stacked area chart: people living in extreme poverty by region, 2000 to 2045, projected after 2025. The global total falls from about 2,000 million to under 900 million, with sub-Saharan Africa becoming the largest share.

900m

living on <$3/day in 2045

330x more likely to be living in low-income countries

Children are still dying from preventable causes

Under-5 child deaths

2000–2045 (millions)

Line chart: millions of under-five child deaths worldwide falling from about 8.5 in 2000 to under 3 by 2045, projected after 2025.

3

million

children < age of 5

will still die each year from diseases we already know how to prevent.

Behind each of these data points is a make-or-break moment for a family: a diagnosis made or missed, a harvest saved or lost, a child who learns to read or doesn’t. AI is arriving at a moment when conflict is growing, diseases are surging, and resources are constrained.

This is why I care about AI. Not because it can write a beautiful line of code or plan your vacation for you, but because it can help a student get unstuck before they fall behind. It can help a farmer save their crop before it fails. And it can help parents get answers about their child’s lingering fever—before it becomes a life-threatening emergency.

Of course, healthcare workers, farmers, and teachers should continue to bring their unique judgment, creativity, and care to their work. But designed well, AI can give them more support, helping knowledge travel farther, faster.

Health and opportunity gaps between the richest and poorest people will stay stubbornly wide unless something changes dramatically.

©Gates Archive/Saumya Khandelwal, India

Left to the market, AI will be designed by and for the richest people in the world.

I believe that the decisions made in the next 12 to 18 months—about how AI is built, funded, and deployed—will determine whether this technology primarily benefits the people who already have the most or reaches those who have the least.

We can harness AI for good. But it won’t happen by accident. After all, the market is an extraordinary engine of innovation, but a terrible guarantor of equal opportunity.

The Gates Foundation was created in part to address a basic market failure: the people with the greatest needs often have the least power to shape where innovation and investment go. For decades, that has meant lifesaving vaccines, medicines, and other tools reaching the world’s poorest communities years later than they reach wealthier ones—if they reach them at all. Much of our work has been about closing that gap.

©Gates Archive/Thomas Omondi, Kenya

AI presents the same challenge, only at much greater speed. Left to the market alone, the most capable tools will be built first for the people and institutions most able to pay for them—not necessarily for those who could benefit most.

That is how inequity compounds: the technology that makes life easier, increases productivity, and sometimes even saves lives arrives first for people who already have the most.

With AI, the gap could grow even faster. AI is spreading more quickly than earlier waves of computing. With every month that passes, the distance grows. AI models get sharper and more capable for the people already using them, while standing still for everyone else.

That is what makes this moment so urgent. Things are changing rapidly, and the window to shape who benefits from AI, and how soon, is short.

The good news? Making AI useful for the people and places where it could make the biggest difference is not an evolutionary leap. It’s within reach.

But it requires a deliberate, specific commitment from government leaders and the companies developing the technology: to measure success not only by what AI can do for the most profitable users but by what it can do for the people who stand the most to gain.

Where thedifferencegets made

There are only so many doctors, teachers, and farmers' advisors in the world. AI could give each of them the support to do more.

©Gates Foundation/Mike Lawrence, United States
©Gates Foundation/Mike Lawrence, United States

We need to reach the people who have the most to gain from AI.

Today, many of the most promising AI tools are still in pilot mode. They need to work in all languages, on ordinary phones, and in the places where people make real-life decisions before they can be trusted to take to scale. But the distance between pilot mode and scale could be much shorter than it was for earlier technologies—because if done well, AI can reach people through natural conversation, on a device already in their pocket. That means the choices being made now—by AI labs, governments, philanthropies, and local leaders—matter enormously.

Within three years, if we make the right choices, the most promising AI tools will be broadly deployed where they can make the biggest difference on most people’s lives and livelihoods in health, agriculture, and education.

©Gates Archive/Mansi Midha, Indonesia

Across these critical issues, people in the most remote, lowest resource settings should have access to the best possible guidance at the same time as people with the most resources. To get there, AI will need to be affordable and will have to earn people’s trust by protecting their privacy and working in their specific contexts.

That will require significant commitment from AI companies, governments, philanthropists, and many others. The Gates Foundation is ready to do our part. In fact, we’re already working with others to ensure the potential benefits of this technology reach everyone.

Where the difference gets made: A nurse, a farmer, a teacher

The next two decades can be a period of extraordinary progress for millions of people.

But ensuring more moms and babies survive childbirth and childhood, more families grow their incomes, and more students achieve their goals will require more than new technology. It will require getting the right knowledge to more people at the moment they need it.

I believe human ingenuity should never be replaced. But there are only so many doctors, teachers, and advisors in the world, and they simply can’t reach everyone who needs support.

The shortage of doctors and trained health workers in low-income countries is dire.

In sub-Saharan Africa, one doctor cares for roughly 2,000 people. That means a single doctor is often responsible for the health of an entire town. That means many families who need urgent help have to wait, or don’t get care at all. In high-income countries, each doctor cares for fewer than 200 people. To come close to that amount of access in Sub-Saharan Africa, the region would need more than two million additional doctors. That gap will narrow over time—but far too slowly to meet the need that exists right now.

sub-sahara africa

high-income countries

One

Doctor

2000

people

*Each person represents 100 people

Training more doctors and health workers is essential. But patients cannot afford to wait. We need to give today’s nurses, community health workers, and other frontline providers the capacity to reach more patients, and give more informed advice based on all the expertise AI can make accessible.

Gaudence Ngendahayo, the community health worker I met in Rwanda, is also a farmer. That’s not unusual. In many communities, the people delivering basic health care are also earning a living, feeding their families, and taking care of everything else life demands.

Each day, farmers like her make dozens of seemingly small decisions. What to plant, when to plant it, and where? How much seed to buy? Whether to irrigate, how to respond to a pest, when to harvest, and when to take a risk and do things differently?

For many of the world’s farmers, especially those with small plots of land, getting those decisions right can mean the difference between a good harvest and a lost season.

But getting them wrong does not just cost a crop. It can cost a family its income, its food supply, and its savings.

And yet, in much of the world, when a farmer is facing a failing crop, a persistent pest, or weather that is more extreme than it used to be, there’s simply no one to call for advice. That’s not just a problem for their family; it’s a problem for everyone who relies on them for food.

Earlier this year, on a farm in Andhra Pradesh, India, I watched a farmer named Annapurna Devi take a picture of a diseased banana leaf with her phone. Using an AI tool, she received a diagnosis in her own language, along with a recommendation for how to treat the pest attack. Annapurna ordered a drone via the application to spray the recommended pesticide, which was deployed to her fields within 48 hours.

The technology was impressive. But what mattered most was much simpler: Annapurna needed to know whether she was going to lose her crop. The AI tool gave her the answer in time so she could save it.

In some parts of the world, a single teacher manages 60 or more students in each classroom, often across multiple grade levels—with almost no tools to know which children are falling behind until it’s too late. AI’s promise in education lies in its ability to amplify teachers’ impact, helping them better understand where students are struggling, tailor support to individual needs, and spend more time on the complex, emotional work of educating the next generation. It can also serve as a tutor for every child, extending that same personalized attention beyond the classroom.

Today, a teacher might have their students fill out a worksheet or problem set, collect them at the end of class, go home, grade them, and then use the results to inform the next day’s lesson plan. It’s a lot of tedious work, but it’s necessary if they want to tailor their next lesson to the needs of every individual student.

AI tools are making that easier. Instead of collecting worksheets at the end of class, a teacher can collect them mid-lesson and, with the help of AI, get real-time guidance on who’s struggling with which concepts, and how to help.

I believe human ingenuity should never be replaced.

Make AIMatter forEveryone

We still have a choice. We can let AI follow the old pattern, where the people with the most resources benefit first and most. Or we can make a different choice: build it for people everywhere.

  1. 01Work in every language
  2. 02Grounded in local data, built for local realities
  3. 03Invest in people and access

Three things we need to get right

If AI is going to help improve the lives and livelihoods of the world’s poorest people, a few things need to happen in the next 12 to 18 months—and there are steps AI companies, governments, philanthropists, and many others can take right now.

I’m not suggesting there’s a magic three-step solution that will make AI accessible and beneficial for everyone. But these are three fundamental building blocks we can prioritize now to make a big difference.

01Work in every language

First, AI tools have to work in every language people speak.

Most of the world is invisible to today's AI models. These AI systems were trained primarily on what exists on the internet—and the internet reflects a deeply unequal world. More than 90 percent of the data used to train early, large language models came from English-language sources. The communities that stand the most to gain from AI are largely absent from the knowledge base these tools are built on. They are not just underserved. They are not in the picture at all.

Every month, AI gets sharper and more capable for the one billion (mostly English-speaking) people who already have access and stays the same for the other seven billion people on earth.

In English, leading AI speech recognition systems make errors less than 6 percent of the time. In Yoruba, that same system fails more than 60 percent of the time.

It is not just everyday vocabulary that matters. A striking example comes from Malawi, where a woman in labor might say her “water has broken” in Chichewa. Translated directly to English, it would sound like she has simply “thrown away water.” That mistranslation could be the difference between timely, urgent medical advice and tragedy.

If AI is going to improve health on a continent with more than 2,000 languages, it has to understand what and how people actually speak—their dialects, accents, and slang, too.

This requires investment in local language datasets and evaluations tailored to how people use language in the places these tools are meant to serve.

02

Grounded in local data, built for local realities

Second, these tools have to be grounded in local data and built for local realities.

Language is only the beginning. An AI tool built for a commercial farmer in Iowa is not necessarily ready for a smallholder farmer in Ethiopia. Smallholder farmers often grow several crops on a small plot, rely on family labor, have limited access to credit and insurance, and make decisions in conditions where a failed harvest can threaten both their income and their family’s food supply. An effective tool must understand that reality—as well as the crops they grow, the pests they face, the cost and availability of inputs in local markets, and what the weather is likely to do in their specific region.

The same is true in health. Global health guidelines already exist. The work now is making those guidelines locally relevant: incorporating the right epidemiology for a specific country, the treatment options actually available in that health system, the operating conditions of the clinics where health workers are making decisions. A community health worker in Rwanda examining a baby with a fever faces different questions than a clinician in a well-resourced hospital—and the AI supporting her needs to reflect that.

This is a data problem as much as a design problem. Building AI that works in every setting requires collecting the right local data—health records, farming outcomes, weather trends, student learning patterns—from the places and populations the tools are meant to serve. Countries should be able to decide on the best way to manage and protect that data, including how it is stored and on what terms it is shared, and with whom.

Both closed and open AI systems have a role to play—what matters is not which model a country uses but whether it works for their specific needs, at a cost and on terms they can sustain.

The good news is that this investment compounds over time. AI systems that start with good local data and are tested against real-life decisions—not just impressive demonstrations—get better the more they are used. People trust tools they’ve personally tried. The real test isn’t whether it sounds impressive, but whether it actually works.

03

Invest in people and access

Third, invest in the people and access to make AI work.

Whether a promising AI pilot actually helps millions of people depends on two things the world isn't investing in enough: people and access.

On people: the communities with the most to gain need local AI leaders at every level—policymakers and technologists who shape how AI enters their countries, and doctors, agronomists, teachers, and data scientists who understand these tools deeply enough to evaluate them, adapt them, and hold them accountable. That means creating real pathways to technical AI expertise in these countries: college programs, master's degrees, and hands-on training that give the next generation the knowledge to lead this work themselves, not just use the tools someone else built.

On access: none of this is possible without meaningful access to AI models and the ability to run them where they're needed. Right now, that access is highly concentrated in wealthy countries—and the barriers go beyond price alone. Changing that will require a mix of approaches: price reductions, commitments from AI companies to make their models and computing resources available, and government and philanthropic funding, among others.

The goal is not for AI to be designed somewhere far away and handed to communities to figure out. It is for the communities with the most to gain to develop the knowledge and the agency to shape how it works for them.

Making this matter for everyone

A world of greater productivity should make people's lives better. But history has shown us that greater productivity and more resources do not automatically produce better life outcomes—more purpose, ease, or peace. The choices being made now about AI are choices about what kind of world we want to leave the next generation.

Imagine what that could look like.

A pregnant woman could ask a question in her own language at home, late at night, without traveling miles to a clinic. A farmer could catch one sick plant before it becomes a lost harvest—and another season of hunger. A student who gets stuck on a lesson could get help getting unstuck, while her teacher gets a clearer picture of where she needs support.

These are not small things. They're the moments that decide whether progress reaches a family when it matters, or not at all.

AI is moving fast. The market will not wait. The systems being built now will shape who benefits for years to come.

Jashodaben, a creche worker, engages with her grandchild in her home at end of a work day in Ahmedabad, Gujarat, India.

We still have a choice—AI companies, governments, and philanthropies. We can let AI follow the old pattern, where the people with the most resources benefit first and most. Or we can make a different choice: build it for people everywhere.

Let's make this matter.

Real Work.Real Impact.

Four AI tools. Four frontline workers. Already making a difference.

©Gates Archive/Mansi Midha, India

Early evidence shows what's possible.

The people making critical decisions every day—on farms and in hospitals, classrooms, and communities—see the challenges and opportunities of AI even more clearly than I can. They know what it means when a mother has no expert clinician nearby, when a farmer has no one to call, or when a teacher is trying to help 30 students who are all struggling in different places.

Early evidence suggests that tools built for frontline workers are already making a big difference. Here are four tools I’m excited about—ones that show what becomes possible when frontline workers have the support they need.

PENDA HEALTH

+16percentage points

In diagnostic accuracy

KIDDOM ATLAS

+6months

of additional learning in a single school year

GEMINI GUIDED LEARNING

+1.7years

of learning

MahaVISTAAR

<0.18cents

per farmer

Guest Essays

In the essays that follow, you’ll hear from four people using these tools in health, agriculture, and education. They understand not only what AI can do but what it will take to make it useful in the real world.

  • I’m still the provider. AI has my back.

    By Vyonne Njeri

    In Nairobi, Kenya, a network of affordable primary care clinics called Penda Health has embedded AI directly into clinical visits—giving clinicians real-time guidance on diagnosis, drug dosing, and when to escalate as they see patients. The clinician sees the suggestion and decides when and how to act. Diagnostic accuracy has improved by 16 percentage points.

  • A student works on an assignment using the Kiddom app during a class at the PS 083 Donald Hertz School in The Bronx, New York.

    Meeting every student’s needs

    By Arnelle Banks

    In the United States, the AI tool Kiddom Atlas helps teachers determine how to best support each student to unlock math skills and move forward. Students complete a short diagnostic test at the end of each lesson; Atlas gauges each student’s level of understanding and delivers a ready-made plan for the next lesson to the teacher. In early pilots across 21 middle schools, grade 7 students gained the equivalent of six additional months of learning in a single school year.

  • A tool that’s helping change the numbers

    By Momodu Orgber Bah

    In Sierra Leone, teachers using an AI tool called Gemini Guided Learning are getting a new kind of support in the classroom. While teachers lead the lesson, students can turn to Gemini for help with math. When students ask for an answer, the tool responds with a question to help them work through and understand the concepts. In an eight-week trial across 12 schools, students made the equivalent of up to 1.7 years of typical learning progress.

  • Better advice, better harvests

    By Dipali Kalbhor

    And in India, the state of Maharashtra has built a free AI advisory service for farmers—called MahaVISTAAR—directly into government infrastructure. Through an app, voice calls, and chat, farmers can ask questions about their crops, the weather, and pest management in their own language and get verified, localized answers. More than 740,000 farmers have already joined, with 70,000 new users signing up every month. This service costs the government less than 18 cents per farmer.

A FinalThought

AI can make the difference

A diagnosis made

A harvest saved

a child who learns to read

Jashodaben, a creche worker, engages with her grandchild in her home at end of a work day in Ahmedabad, Gujarat, India.

Track the Sustainable Development Goals

In 2015, 193 world leaders agreed to 17 ambitious Sustainable Development Goals (SDGs) to end poverty, fight inequality, and improve health by 2030. Goalkeepers works to accelerate progress toward these goals, focusing on Goals 1 to 6.

Each year, the Goalkeepers Report tracks 18 key indicators—from poverty to education—offering the latest estimates on where innovation and investment are driving progress, and where we're falling short. These data remind us that progress is possible but not inevitable.

With just four years left, the world is off track. It’s clear: urgent action is needed to meet the SDG targets and create a more equitable, safer future for all by 2030.

No Poverty

Poverty

Population below the international poverty line
Zero Hunger

Stunting

Stunting among children under age 5
Zero Hunger

Agriculture

Rate of income growth of small-scale producers
Good Health & Well-Being

Maternal Mortality

Maternal deaths per 100,000 live births
Good Health & Well-Being

Under-5 Mortality

Under-5 child deaths per 1,000 live births
Good Health & Well-Being

Neonatal Mortality

Neonatal deaths per 1,000 live births
Good Health & Well-Being

HIV

New cases of HIV per 1,000 people
Good Health & Well-Being

Tuberculosis

New cases of tuberculosis per 100,000 people
Good Health & Well-Being

Malaria

New cases of malaria per 1,000 people
Good Health & Well-Being

Neglected Tropical Diseases

Prevalence of 15 NTDs per 100,000 people
Good Health & Well-Being

Family Planning

Percentage of family planning needs met with modern methods
Good Health & Well-Being

Universal Healthcare Coverage

UHC effective coverage index score
Good Health & Well-Being

Smoking

Prevalence among people ages 15 and older
Good Health & Well-Being

Vaccines

Coverage of DTP (third dose)
Quality Education

Education

Children who cannot read and understand a simple text by age 10
Gender Equality

Gender Equality

Gender Index score
Clean Water and Sanitation

Sanitation

Population using safely managed sanitation
Decent Work and Economic Growth

Inclusive Financial Systems

Adult financial account ownership

Download the 2026 Goalkeepers report

Select your preferred language

Real Work.

Real Impact

I’m still the provider. AI has my back.

By Vyonne NjeriClinical Officer at Penda Health, Nairobi, Kenya

The place that inspired me to study medicine wasn’t a hospital—it was a church.

It was during a visit to see my uncle, a priest in a small, East Kenyan town called Modogashe. One day, I went to the parish to bring him his lunch and saw a nurse performing a suture on an elderly woman’s leg. I looked outside and saw a long line of patients, all waiting for their turn. His parish housed a small local clinic, staffed by a single nurse—the only medical practitioner in the area.

I turned to my uncle right then and told him I wanted to study medicine.

In rural communities like Modogashe—or Laikipia County, where I grew up—many patients don’t live anywhere near a clinic. Those who do often can’t afford regular visits, so they develop chronic, preventable diseases that cost even more to treat in the long term.

Clinicians, meanwhile, face overwhelming patient loads, compounding the stress of a job where a single mistake could cost someone their life. That’s why the clinic I’m at now, Penda Health, built a safety net.

©Gates Archive/Brian Otieno, Kenya

It’s called AI Consult: a copilot embedded directly into the same digital records system we use for every appointment. As we enter information on the patient’s medical history, symptoms, test results, and our recommended treatments, the tool double checks our work. Most of the time, it shows a green check mark. But if we overlook a certain symptom or prescribe an inappropriate medication, it might return a yellow or red flag, prompting us to take another look.

I once had a mother bring in her four-month-old with a fever and runny nose. It looked like a simple cold or allergies, so I prescribed acetaminophen. But when I typed that in, a yellow flag from AI Consult popped up: the child’s respiratory and heart rates were elevated. That could just be the fever, but it could also be something else. So I listened to the child’s chest again—and this time, I heard something concerning.

I told the mother she should have her child screened for congenital heart disease. When I followed up a week later, she said their pediatrician had indeed found a congenital defect—a hole in the heart. If this hadn’t been caught, it could have been deadly. But with the right treatment, most children with these kinds of heart defects live healthy, normal lives.

Across all our clinics, we’ve seen a 16 percent drop in diagnostic errors and a 13 percent reduction in treatment errors since we started using AI Consult.

AI can’t replace health workers… But when you’re the sole person responsible for someone’s health, a second opinion can save someone’s life.

It’s still far from perfect. I have some concerns about newer clinicians over-relying on AI instead of developing their own clinical judgment. And I’ve occasionally noticed that certain information—like local drug formularies or protocols for pediatric care—is missing from the tool.

AI can’t replace health workers. I was the one who examined that baby. I was the one holding the stethoscope and talking to the mother. But when you’re the sole person responsible for someone’s health, a second opinion can save someone’s life.

A student works on an assignment using the Kiddom app during a class at the PS 083 Donald Hertz School in The Bronx, New York.
Arnelle Banks was not available for photography or filming. Her essay appears in her own words.

Meeting every student’s needs

By Arnelle BanksTeacher, New York City, United States

No middle schooler wants to feel like they’re different from all the other kids. But I was. I was born and raised in Jamaica and moved to New York in seventh grade. Thankfully, I had a math teacher, Ms. Nolan, who never made me feel like an outsider. She even talked to the other kids to make sure I never sat by myself at lunch.

Today, I teach seventh grade math too. Many of my students are like me: immigrants from another country who might be enrolling in a U.S. school or learning English for the first time. But my classes are structured like any other.

When students walk in, there’s a warm-up problem on the board. Then we move on to a lesson, followed by independent or group practice. And we close with an “exit ticket”: one last question to see if they understood the lesson, which they hand to me on their way out. After class, I go through their exit tickets by hand to see who understood the concepts, and who’s struggling.

Like many teachers, my greatest challenge is differentiation. Because my classes are fairly large, I tend to adapt lessons for the students who are struggling, so I can make sure they catch up before we introduce a new concept. But that sometimes means I’m not doing enough to challenge the students who are already at or above grade level.

That’s where an AI tool, Kiddom Atlas, made a huge difference. Now, instead of giving everyone the same warm-ups and exit tickets, Kiddom tailors those problems to meet students where they are. If they’re struggling with a concept, it gives them more practice on it. If they’ve got it, it challenges them with something harder.

A student works on an assignment using the Kiddom app during a class at the PS 083 Donald Hertz School in The Bronx, New York.
©Gates Foundation/Mike Lawrence, United States

At the end of each day, it gives me summaries of each student’s progress, and can even show me precisely where there are gaps in their understanding. For instance, it showed me that one of my seventh graders was actually struggling because of a concept they misunderstood from the fourth grade. I don’t think I would have realized that by myself.

Before Kiddom, I was always skeptical about doing math on a computer. As teachers, we know that when students write things down by hand, they’re more likely to internalize the lessons and procedures. Even now, kids still spend most of my period with pencil and paper. But just using Kiddom for warm-ups and exit tickets—about 5 minutes of class time each—has made a huge difference.

Kiddom tailors problems to meet students where they are.

I used to be known as the “bag lady” who always went home with a pile of ungraded papers sticking out of my bag. Now, the only thing in there is my laptop.

More important, my students are excelling. Over a single year, I’ve seen all of them rise to or above grade level. And though they don’t know it yet, by the time you read this, many of my once-struggling seventh graders will have started their eighth grade honors class.

There are still issues to work out. I wish my students could annotate questions on the computer the same way I teach them to on paper. And I sometimes catch flaws with the practice questions themselves. Kiddom has always been responsive when I raise these issues, while I can’t say the same for every platform.

AI can never replace a real teacher. It can’t make a classroom feel like a home, or make sure a new student doesn’t sit alone at lunch. But even though no middle schooler wants to feel different from everyone else, the truth is, they are. Every student has unique needs. And Kiddom Atlas has helped me support those needs better than I ever could have alone.

A tool that’s helping change the numbers

By Momodu Orgber BahTeacher, Lungi Town, Sierra Leone

When I was a student, it was rare to see a textbook.

In the 1990s, when I was in secondary school, my country was in the middle of a civil war. Resources were scarce, and my education was constantly interrupted. Every once in a while, my classmates and I would see a rich kid walking around with a textbook, and we’d try to steal a few pages out of it so we could study too.

After the war, I studied masonry. But after working a few odd jobs, a local Catholic priest—Father Dominic—took a chance on me and asked me to teach construction to ex-combatants.

As it turned out, teaching was my calling. Today, I teach mathematics at a junior secondary school that not only has a library full of textbooks but a computer lab. I couldn’t be happier; my students have access to technology. So when I was invited to a workshop hosted by EducAid Africa about Gemini’s Guided Learning AI tool, I was excited about what it could mean for my class.

The impact was even greater than I imagined.

In Sierra Leone, people think of mathematics as something that only geniuses study. This belief has created a real problem for the entire country. After all, mathematics is a core subject for university and a foundational skill for countless professions.

I’ve been teaching mathematics for 20 years now, and usually, only around 55 to 60 percent of my students pass the class.

Since we started using AI to support our instruction, that number has gone up to 100 percent.

©Gates Archive/Abdul Kanu, Sierra Leone

It’s not just my class. In a recent pilot program, students who used Guided Learning saw one to two years of educational progress in just eight weeks. Students—including mine—also reported enjoying math more, and I’ve seen them engage with the lessons more deeply.

The AI Guided Learning tool is fairly easy to use. Every day I write the topic of our lesson on the whiteboard. Students enter that topic, as well as their country and grade level, into the program, and it gives them guided practice questions. While they work on the questions, I make my rounds and check on anyone who seems to be struggling. Once they’re done, we leave the computer lab, head back to the classroom, and I continue teaching as usual. So what they don’t learn from Gemini, they learn from me—and vice-versa.

What really excites me are the applications beyond the classroom. If my students want to get more practice in, I tell them to borrow their parents’ phones—with supervision from their parents, of course—and use Gemini like a personal tutor.

I couldn’t be happier my students have access to technology. The impact was even greater than I imagined.

There are still challenges when it comes to AI, of course. I worry about how kids might use these technologies without supervision. And a lot of my students don’t have internet access at home, so they couldn’t continue studying even if they wanted to.

But then I think back on my own childhood. Not only did I not have internet access, I often didn’t even have light. I remember studying outside by moonlight when I couldn’t find a lantern. I’m so excited about the opportunities my students have today; opportunities I could never have imagined as a student.

Better advice, better harvests

By Dipali KalbhorSmallholder farmer and entrepreneur, Maharashtra, India

Farming is in my blood. I come from a family of farmers. I graduated university with a degree in agriculture, and my first venture after graduating, raising goats, was a success. I thought I knew everything. So I took my savings and started a poultry business. It was a disaster. I ran into one problem after another. And before I knew it, I’d lost thousands of chickens.

After that, I fell into a deep depression. It took a while to pull myself back out. But thankfully I wasn’t alone. I have a great team behind me. With their support, I started a new business: beekeeping. We began with just 30 bee boxes. Today, we have more than 1,000 boxes, and a team of 12 people. We sell to local businesses and individuals and even provide pollination services to other farmers—reducing their reliance on other regions for pollination.

I’ve learned a lot—both in the field, and in the classroom. But one of the biggest lessons is that you can only do so much alone. If you want to really grow, you need not only a good team, but access to the right information too. That’s why I was excited to learn about MahaVISTAAR during a regular training session with our local agricultural extension officer.

©Gates Archive/Ryan Lobo, India

MahaVISTAAR isn’t the first AI tool or app I ever tried, but it is the first dedicated specifically to farming. It provides advice on crop prices, changing weather patterns, and pest control. But my favorite part might be the fertilizer calculator. All I have to do is put in what I’m growing and what kind of compost I’m using, and it tells me exactly how much fertilizer to use and when. That single feature has helped me reduce fertilizer usage by around 15 percent—savings that allowed me to start another new business growing mulberries, which I sell to local silk farmers.

Since the app was developed by the government, it understands numerous local languages and dialects. I can even use it to check what government support programs I’m eligible for, compare the selling prices of different crops, look up contact information for local agricultural officials, and so on. I probably use it to help me make decisions at least two or three times a day.

One of the biggest lessons is that you can only do so much alone. If you want to really grow, you need not only a good team, but access to the right information.

I’m one of the youngest farmers in my region. You might think that would be a disadvantage, but I’ve learned the hard way that you can’t build a business like this alone. You need a good team and the right tools. As long as you have both, there’s no failure you can’t overcome.

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