An early childhood center director in Washington re-draws the weekly menu to contend with the rising cost of food. A home-based provider in Arkansas stays up late crunching the budget numbers, stretched thin between food and health insurance. A provider who watches children of her friends, family members and neighbors has a sleepless night worrying about if the growing children have enough to eat.

Discussions have long swirled around children’s food insecurity, most recently peaking with the paused Supplemental Nutrition Assistance Program (SNAP), formerly known as the food stamps program. That program is reinstated, though its requirements are changing by the end of next year.

Now, a new study has found that children’s caretakers, too, are struggling with hunger more than ever.

According to a survey from the RAPID Survey Project at the Stanford Center on Early Childhood, more than half (58 percent) of child care providers surveyed experienced hunger in June 2025 — which is one of the highest percentages the organization has seen since it began collecting data in 2021. Some respondents – including the providers from Washington and Arkansas mentioned previously – shared personal experiences with hunger, such as those mentioned above.

“I think most people think we’re in a very prosperous country and with hunger, there’s something of a mindset around it of abject poverty,” says Philip Fisher, director of the Stanford Center for Early Childhood and founder of the RAPID Project. “But we’re starting to talk about these issues as a canary in the coal mine; there’s signals it’s starting to spread to a much wider swath of population.”

The latest findings are a bump from June 2021 to May 2025, when an average of 44 percent of the surveyed child care providers reported experiencing hunger. The spike, while alarming to Fisher, was not surprising. The cost of groceries is rising, and there have also been cuts to programs that provide food for food banks.

“It’s likely people are seeking free food where it’s available, but that is becoming harder and harder to access,” Fisher says, adding his son, who runs a food bank in rural Washington state, has seen an increased demand.

“Food insecurity” is defined by the U.S. Department of Agriculture as a household’s access to adequate food. The RAPID Project considered a respondent “hungry” if they experienced at least two of the five food insecurity scenarios:

• If the food you bought went bad, could you not afford to replace it?
• Can you not afford to eat balanced meals?
• Did you or other adults in your household ever cut the size of your meal or skip meals because there wasn’t enough money for food? If yes, how often did this happen?
• Did you ever eat less than you felt you should because there wasn’t enough money for food?
• Were you ever hungry but didn’t eat because there wasn’t enough money for food?

Poonam Gupta, a research associate at the Urban Institute in the Tax and Income Supports division, says while “hunger” is an immediate condition, the usage of the “food insecurity” label showcases a longer-term issue.

“Even if you’re not currently hungry in the moment while taking the survey, you can still be food insecure,” she says.

The reported food insecurity spanned across all types of child care providers. Fisher said center-based directors had the lowest rates of food insecurity among the group, followed by home-based providers, with center-based teachers and providers of family, friends and neighbor care seeing the highest rates.

“There’s been a ton of reporting on how broken the child care system is and a lot has to do with low wages,” he says. “So it’s not surprising it’s across the board.”

Gupta added early childhood education teachers, and other low-income workers with hourly wages, are in a particularly tough spot because of the instability that comes with weekly schedules.

“A lot of attention is paid in general to the low-income workforce, but folks that have non-traditional or inconsistent hours tend to be at the highest risk,” she says. “There’s a type of instability in not being able to predict when your hours are. And if you’re an early childhood education provider and have children of your own, it’s difficult for you to afford child care and even more difficult to meet the work requirements to get SNAP benefits.” The program requires recipients to work at least 80 hours per month.

Early childhood care providers’ schedules can be particularly volatile, where workers can get sent home if, for example, not enough children are in attendance on a given day.

Hunger is widespread for children and adults, with 1 in 4 households experiencing food insecurity in 2024, according to the Urban Institute. But Fisher and Gupta believe the general public isn’t well aware of hunger among adults. They pointed out that most efforts — both from organizations and in research projects — largely focus on children’s food insecurity.

“I think the images they show perpetuate these ideas that the people who are hungry are in an extreme margin,” Fisher says, rather than the reality that much of the general public is at risk for going without adequate food.

The issue points to an irony at the heart of how child care works in the U.S. Child care costs families more than ever, with Child Care Aware of America finding that parents in most states pay more for child care each month than for rent or mortgages. However, according to the Bureau of Labor Statistics, the average pay for a child care worker is below $12.25 per hour. Child care workers with a college degree get roughly $14.70 per hour.

Gupta estimates that food insecurity, including among early childhood care providers, will get worse. Upcoming changes to SNAP are slated for October 2026 and include an increased age limit and removing exemptions for veterans, those experiencing homelessness and those aging out of the foster care system. According to 2024 data from the Center for the Study of Child Care Employment at the University of California at Berkeley, roughly 43 percent of child care workers receive some form of public assistance, including SNAP and Medicaid.

“If anything, we only expect this to get worse in the coming year because of all the disruptions being made, mainly to the SNAP program,” she says.

This story was published by a Voices of Change fellow. Learn more about the fellowship here.

I’ve attended my share of professional development sessions as an educator. Too often, I’ve walked away asking the same question: Is this really how we expect teachers to learn?

I still remember one session on trauma-informed teaching held in a school cafeteria. The tables and attached seats were too small for most of us, while the lights hummed overhead. For two and a half hours, the facilitator read from an endless slide deck about the importance of connection and empathy. There was minimal context building, limited discussion and no reflection. By the end, the facilitator smiled and said, “Now you are all trauma-informed teachers!” I think my eyes rolled so far back they almost stayed there.

Sitting and listening to someone talk for 45 to 60 minutes is not learning, let alone two and a half hours. My body knows it before my brain does. I get restless, my mind drifts, I check the time and take a walk to refill my water bottle. In that first hour, disappointment sets in fast.
Minimal conditions for adult learning have become the norm. I used to resent that; now I fear it. Because the longer I sat in those breakout rooms, the quieter I became. My curiosity dulled, the topic’s urgency faded and I started doing what was expected: showing up, signing in and leaving seemingly unchanged. That terrified me. I could feel myself becoming the kind of learner I never wanted my students to be. Even the most dedicated teachers can wilt in the wrong conditions.

And here’s what often goes unspoken: teachers already give so much of their time to these sessions, spending hours after school on professional days and during planning periods. That investment deserves more than compliance-based sessions that leave teachers unchanged or walking away with a checklist of “next steps” that never take root.

After experiences like that, I find myself returning to familiar questions: Why do we accept for ourselves what we would never accept for our students? What if we taught students the way we teach teachers? We’d call it ineffective, parents would complain and administrators would intervene. Yet, the same approach is accepted for teachers’ professional development: lecture-heavy, one-size-fits-all and compliance-driven. I knew better for my students and kept accepting less for myself. That contradiction began to haunt me.

As a high school English teacher, I built lessons around engagement, differentiation and relevance so students could connect learning to their lives. They deserved instruction that met them where they were and balanced support with challenge. When it comes to teaching teachers, we need the same shift — from professional development done to us, to professional learning created by, for and with us.

A Different Way Exists

I remember one of the first times I felt what real professional learning could be. Around 2013, when Edcamp was spreading across schools, my administrators used this format for one of our PD days. These grassroots “unconferences” turned the familiar model upside down. There were no pre-approved presenters or hour-by-hour agendas. Teachers built the schedule on the spot and moved freely between conversations. If one wasn’t helpful, you left and found another. The emphasis was on curiosity and choice.

I led two sessions that day: one on digital tools for learning and another on equitable grading. I didn’t stand in front of the group; I sat in the circle. We tested tools in real time, pried into long-held grading beliefs and argued about what being fair really means in high school grading. What I remember most wasn’t the content but the energy in the room and the buzz of teachers thinking, building, disagreeing and learning together.

It was the first time I realized how much trust professional learning requires: trust in teachers’ intelligence, instincts and creativity. We talk so much about empowering students, but rarely about empowering teachers. Edcamp, brief as it was, made me wonder what would happen if we trusted educators the way we expect them to trust their students.

That lesson deepened through the Rhode Island Writing Project at Rhode Island College, where “teachers teaching teachers” wasn’t a slogan but a practice. During the summer institute, I joined a community of educators from across the state. We wrote together, shared feedback and listened, really listened, to each other’s classroom stories and the complex and messy overlap between personal and professional life.

That summer changed me. I saw what it meant to honor teacher knowledge, and to treat professional learning as a dialogue, not delivery. It ruined me, in the best way. Once you’ve experienced learning that is alive, reciprocal and demanding, it’s hard to sit quietly again while someone reads from slides.

But here’s what I know: those moments were rare. Outliers.

Most professional development since that summer has looked more like paperwork than pedagogy. Neatly packaged, disconnected and efficient to a fault. For many educators, PD is still something that happens to them, not with them. I’ve seen what that does. It breeds cynicism and convinces brilliant teachers that their professional growth is optional, even disposable. Novice and veteran teachers alike found ways to get through or get by during especially disconnected sessions. Not out of defiance, but self-preservation.

As a district administrator, I find myself in a very different position where I receive more structured opportunities for professional learning than I ever did in the classroom. I attend multi-day workshops on leadership frameworks, statewide coaching institutes and even regional conferences focused on instructional design. They’re well-planned, reflective, energizing and respectful of participants’ needs. Nothing like the one-off PowerPoints teachers sit through during the school day or after school.

That contrast is hard to ignore. It reminds me just how uneven our systems can be. The higher up you go, the more development you’re offered; the closer you are to students, the less you get.

I carry that discomfort with me every day. I think about the teachers in sessions I once led or attended who expressed their skepticism and tiredness of being told what to value or what new requirement to add to their already stacked list of classroom responsibilities. My job now is to make sure the professional learning I help design never repeats that pattern — that it respects their time, their expertise and their humanity. I don’t want them to feel the quiet resignation I once did.

This problem runs deeper than any one district or leader. A recent report from the Annenberg Institute at Brown University affirmed Rhode Island’s commitment to investing in professional learning. The report highlights state-level efforts such as expanding instructional coaching, building in common planning time and fostering cross-district collaboration. These are the supports I wish I’d had years ago.

The report also reminded me of what I’ve seen firsthand: resources and structures only work when the design honors teachers’ time and trust.

How We Teach Teachers

How we design professional learning makes visible the value we place on teachers. When PD is treated as a formality, the message is that teacher growth is optional. But when it’s treated as authentic learning, the message is clear: adult learning matters, and investing in teachers is investing in students.

If we want professional learning that serves educators and the students they teach, we must move beyond seat time and toward structures that honor teacher expertise and foster continuous improvement. The elements of strong professional learning aren’t mysteries; they mirror the same principles of good teaching.

A few approaches that work include teacher-led inquiry cycles that invite educators to identify problems of practice and design solutions together; offering choice and voice in sessions that make learning relevant and personalized; building in time for application and reflection; and creating job-embedded opportunities where teachers can learn in context alongside their colleagues and students.

The future of our profession will be defined by what we choose right now and whether we can model the kind of learning we say we want for our students.

When completing math problems, students often have to show their work. It’s a method teachers use to catch errors in thinking, to make sure students are grasping mathematical concepts correctly.

New AI projects in development aim to automate that process. The idea is to train machines to catch and predict the errors students make when studying math, to better enable teachers to correct student misconceptions in real time.

For the first time ever, developers can now build fascinating algorithms into products that will help teachers without requiring them to understand machine learning, says Sarah Johnson, CEO at Teaching Lab, which provides professional development to teachers.

Some of these efforts trace back to the U.K.-based edtech platform Eedi Labs, which has held a series of coding competitions since 2020 intended to explore ways to use AI to boost math performance. The latest was held earlier this year, and it tried to use AI to capture misconceptions from multiple choice questions and accompanying student explanations. It relied upon Eedi Labs’ data but was run by The Learning Agency, an education consultancy firm in the U.S. A joint project with Vanderbilt University — and using Kaggle, a data science platform — the competition received support from the Gates Foundation and the Walton Family Foundation, and coding teams competed for $55,000 in awards.

The latest competition achieved “impressive” accuracy in predicting student misconceptions in math, according to Eedi Labs.

Researchers and edtech developers hope this kind of breakthrough can help bring useful AI applications into math classrooms — which have lagged behind in AI adoption, even as English instructors have had to rethink their writing assignments to account for student AI use. Some people have argued that, so far, there has been a conceptual problem with “mathbots.”

Perhaps training algorithms to identify common student math misconceptions could lead to the development of sophisticated tools to help teachers target instruction.

But is that enough to improve students’ declining math scores?

Solving the (Math) Problem

So far, the deluge of money pouring into artificial intelligence is unrelenting. Despite fears that the economy is in an “AI bubble”, edtech leaders hope that smart, research-backed uses of the technology will deliver gains for students.

In the early days of generative AI, people thought you could get good results by just hooking up an education platform to a large language model, says Johnson, of Teaching Lab. All these chatbot wrappers popped up, promising that teachers could create the best lesson plans using ChatGPT in their learning management systems.

But that’s not true, she says. You need to focus on applications of the technology that are trained on education-specific data to actually help classroom teachers, she adds.

That’s where Eedi Labs is trying to make a difference.

Currently, Eedi Labs sells an AI tutoring service for math. The model, which the company calls “human in the loop,” has human tutors check messages automatically generated by its platform before they are sent to students, and make edits when necessary.

Plus, through efforts like its recent competition, leaders of the platform think they can train machines to catch and predict the errors students make when studying math, further expediting learning.

But training machine learning algorithms to identify common math misconceptions a student holds isn’t all that easy.

Cutting Edge?

Whether these attempts to use AI to map student misconceptions prove useful depends on what computer scientists call “ground truth,” the quality of the data used to train the algorithms in the first place. That means it depends on the quality of the multiple choice math problem questions, and also of the misconceptions that those questions reveal, says Jim Malamut, a postdoctoral researcher at Stanford Graduate School of Education. Malamut is not affiliated with Eedi Labs or with The Learning Agency’s competition.

The approach in the latest competition is not groundbreaking, he argues.

The dataset used in this year’s misconceptions contest had teams sorting through student answers from multiple choice questions with brief rationales from students. For the company, it’s an advancement, since previous versions of the technology relied on multiple choice questions alone.

Still, Malamut describes the use of multiple choice questions as “curious” because he believes the competition chose to work with a “simplistic format” when the tools they are testing are better-suited to discern patterns in more complex and open-ended answers from students. That is, after all, an advantage of large language models, Malamut says. In education, psychometricians and other researchers relied on multiple choice questions for a long time because they are easier to scale, but with AI that shouldn’t be as much of a barrier, Malamut argues.

Pushed by declining U.S. scores on international assessments, in the last decade-plus the country has shifted toward “Next-Generation Assessments” which aim to test conceptual skills. It’s part of a larger shift by researchers to the idea of “assessment for learning,” which holds that assessment tools place emphasis on getting information that’s useful for teaching rather than what’s convenient for researchers to measure, according to Malamut.

Yet the competition relies on questions that clearly predate that trend, Malamut says, in a way that might not meet the moment

For example, some questions asked students to figure out which decimal was the largest, which sheds very little light on conceptual understanding. Instead, current research suggests that it’s better to have students write a decimal number using base 10 blocks or to point to missing decimals on a marked number line. Historically, these sorts of questions couldn’t be used in a large-scale assessment because they are too open-ended, Malamut says. But applying AI to current thinking around education research is precisely where AI could add the most value, Malamut adds.

But for the company developing these technologies, “holistic solutions” are important.

Eedi Labs blends multiple choice questions, adaptive assessments and open responses for a comprehensive diagnosis, says cofounder Simon Woodhead. This latest competition was the first to incorporate student responses, enabling deeper analysis, he adds.

But there’s a trade-off between the time it takes to give students these assessments and the insights they give teachers, Woodhead says. So the Eedi team thinks that a system that uses multiple choice questions is useful for scanning student comprehension inside a classroom. With just a device at the front of the class, a teacher can home in on misconceptions quickly, Woodhead says. Student explanations and adaptive assessments, in contrast, help with deeper analysis of misconceptions. Blending these gives teachers the most benefit, Woodhead argues. And the success of this latest competition convinced the company to further explore using student responses, Woodhead adds.

Still, some think the questions used in the competition were not fine-tuned enough.

Woodhead notes that the competition relied on broader definitions of what counts as a “misconception” than Eedi Labs usually does. Nonetheless, the company was impressed by the accuracy of the AI predictions in the competition, he says.

Others are less sure that it really captures student misunderstandings.

Education researchers now know a lot more about the kinds of questions that can get to the core of student thinking and reveal misconceptions that students may have than they used to, Malamut says. But many of the questions in the contest’s dataset don’t accomplish this well, he says. Even though the questions included multiple choice options and short answers, it could have used better-formed questions, Malamut thinks. There are ways to ask the questions that can bring out student ideas. Rather than asking students to answer a question about fractions, you could ask students to critique others’ reasoning processes. For example: “Jim added these fractions in this way, showing his work like this. Do you agree with him? Why or why not? Where did he make a mistake?”

Whether it’s found its final form, there is growing interest in these attempts to use AI, and that comes with money for exploring new tools.

From Computer Back to Human

The Trump administration is betting big on AI as a strategy for education, making federal dollars available. Some education researchers are enthusiastic, too, boosted by $26 million in funding from Digital Promise intended to help narrow the distance between best practices in education and AI.

These approaches are early, and the tools still need to be built and tested. Nevertheless, some argue it’s already paying off.

A randomized controlled trial conducted by Eedi Labs and Google DeepMind found that math tutoring that incorporated Eedi’s AI platform boosted student learning in 11- and 12-year-olds in the U.K. The study focused on the company’s “human in the loop” approach — using human-supervised AI tutoring — currently used in some classrooms. Within the U.S., the platform is used by 4,955 students across 39 K-12 schools, colleges and tutoring networks. Eedi Labs says it is conducting another randomized controlled trial in 2026 with Imagine Learning in the U.S.

Others have embraced a similar approach. For example, Teaching Lab is actively involved in work about AI for use in classrooms, with Johnson telling EdSurge that they are testing a model also based on data borrowed from Eedi and a company called Anet. That data model project is currently being tested with students, according to Johnson.

Several of these efforts require sharing tech insights and data. That runs counter to many companies’ typical practices for protecting intellectual property, according to the Eedi Labs CEO. But he thinks the practice will pay off. “We are very keen to be at the cutting edge, that means engaging with researchers, and we see sharing some data as a really great way to do this,” he wrote in an email.

Still, once the algorithms are trained, everyone seems to agree turning it into success in classrooms is another challenge.

What might that look like?

The data infrastructure can be built into products that let teachers modify curriculum based on the context of the classroom, Johnson says. If you can connect the infrastructure to student data and allow it to make inferences, it could provide teachers with useful advice, she adds.

Meg Benner, managing director of The Learning Agency, the organization that ran the misconceptions contest, suggests that this could be used to feed teachers information about which misconceptions their students are making, or to even trigger a chatbot-style lesson helping them to overcome those misconceptions.

It’s an interesting research project, says Johnson, of Teaching Lab. But once this model is fully built, it will still need to be tested to see if refined diagnosis actually leads to better interventions in front of teachers and students, she adds.

Some are skeptical that the ways companies will turn these into products may not enhance learning all that much. After all, having a chatbot-style tutoring system conclude that students are conducting additive reasoning when multiplicative reasoning is required may not transform math instruction. Indeed, some research has shown that students don’t respond well to chatbots. For instance, the famous 5 percent problem revealed that only the top students usually see results from most digital math programs. Instead, teachers have to handle misconceptions as they come up, some argue. That means students having an experience or conversation that exposes the limits of old ideas and the power of clear thinking. The challenge, then, is figuring out how to get the insights from the computer and machine analysis back out to the students.

But others think that the moment is exciting, even if there’s some hype.

“I’m cautiously optimistic,” says Malamut, the postdoctoral student at Stanford. Formative assessments and diagnostic tools exist now, but they are not automated, he says. True, the assessment data that’s easy to collect isn’t always the most helpful to teachers. But if used correctly, AI tools could possibly close that gap.

Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality.

IBM has adopted the superconducting qubit road map of reaching a 1,121-qubit processor by 2023, leading to the expectation that 1,000 qubits with today’s qubit form factor is feasible. However, current approaches will require very large chips (50 millimeters on a side, or larger) at the scale of small wafers, or the use of chiplets on multichip modules. While this approach will work, the aim is to attain a better path toward scalability.

Now researchers at MIT have been able to both reduce the size of the qubits and done so in a way that reduces the interference that occurs between neighboring qubits. The MIT researchers have increased the number of superconducting qubits that can be added onto a device by a factor of 100.

“We are addressing both qubit miniaturization and quality,” said William Oliver, the director for the Center for Quantum Engineering at MIT. “Unlike conventional transistor scaling, where only the number really matters, for qubits, large numbers are not sufficient, they must also be high-performance. Sacrificing performance for qubit number is not a useful trade in quantum computing. They must go hand in hand.”

The key to this big increase in qubit density and reduction of interference comes down to the use of two-dimensional materials, in particular the 2D insulator hexagonal boron nitride (hBN). The MIT researchers demonstrated that a few atomic monolayers of hBN can be stacked to form the insulator in the capacitors of a superconducting qubit.

Just like other capacitors, the capacitors in these superconducting circuits take the form of a sandwich in which an insulator material is sandwiched between two metal plates. The big difference for these capacitors is that the superconducting circuits can operate only at extremely low temperatures—less than 0.02 degrees above absolute zero (-273.15 °C).

Superconducting qubits are measured at temperatures as low as 20 millikelvin in a dilution refrigerator.Nathan Fiske/MIT

In that environment, insulating materials that are available for the job, such as PE-CVD silicon oxide or silicon nitride, have quite a few defects that are too lossy for quantum computing applications. To get around these material shortcomings, most superconducting circuits use what are called coplanar capacitors. In these capacitors, the plates are positioned laterally to one another, rather than on top of one another.

As a result, the intrinsic silicon substrate below the plates and to a smaller degree the vacuum above the plates serve as the capacitor dielectric. Intrinsic silicon is chemically pure and therefore has few defects, and the large size dilutes the electric field at the plate interfaces, all of which leads to a low-loss capacitor. The lateral size of each plate in this open-face design ends up being quite large (typically 100 by 100 micrometers) in order to achieve the required capacitance.

In an effort to move away from the large lateral configuration, the MIT researchers embarked on a search for an insulator that has very few defects and is compatible with superconducting capacitor plates.

“We chose to study hBN because it is the most widely used insulator in 2D material research due to its cleanliness and chemical inertness,” said colead author Joel Wang, a research scientist in the Engineering Quantum Systems group of the MIT Research Laboratory for Electronics.

On either side of the hBN, the MIT researchers used the 2D superconducting material, niobium diselenide. One of the trickiest aspects of fabricating the capacitors was working with the niobium diselenide, which oxidizes in seconds when exposed to air, according to Wang. This necessitates that the assembly of the capacitor occur in a glove box filled with argon gas.

While this would seemingly complicate the scaling up of the production of these capacitors, Wang doesn’t regard this as a limiting factor.

“What determines the quality factor of the capacitor are the two interfaces between the two materials,” said Wang. “Once the sandwich is made, the two interfaces are “sealed” and we don’t see any noticeable degradation over time when exposed to the atmosphere.”

This lack of degradation is because around 90 percent of the electric field is contained within the sandwich structure, so the oxidation of the outer surface of the niobium diselenide does not play a significant role anymore. This ultimately makes the capacitor footprint much smaller, and it accounts for the reduction in cross talk between the neighboring qubits.

“The main challenge for scaling up the fabrication will be the wafer-scale growth of hBN and 2D superconductors like [niobium diselenide], and how one can do wafer-scale stacking of these films,” added Wang.

Wang believes that this research has shown 2D hBN to be a good insulator candidate for superconducting qubits. He says that the groundwork the MIT team has done will serve as a road map for using other hybrid 2D materials to build superconducting circuits.