This is the latest chapter in what economists – such as Jan Tinbergen, and Claudia Goldin and Lawrence Katz have long called the race between education and technology. Technological advances increase the demand for skills; education systems struggle to keep up. Race 1.0 was largely about whether education could produce enough skilled workers. Race 2.0 is also about whether education can produce the right mix of skills – and continually update them – faster than technology changes work.

What should education systems teach when machines can perform an expanding range of cognitive tasks?

Since 2017, there has been a lot of research on the potential and actual effects of technology, including AI, on employment, skills, and earnings. The question is not simply whether AI destroys jobs. Technology changes tasks within occupations, substitutes for some kinds of work while complementing others and can affect productivity and wages even when employment remains intact. This is particularly challenging because education systems are notoriously slow to change what and how they teach.

The World Bank’s World Development Report 2026, The Promise of Artificial Intelligence, argues that developing countries can harness AI for economic growth, but only if they build the capabilities needed to use it while managing the risks of a widening digital divide. Interestingly, its conclusions about skills substantially resemble the argument I made in 2017. Back then I argued that an alternative approach is to think about the kind of work that technology cannot replace. Today the emphasis is on broad digital literacy, adaptability and core human capabilities rather than simply specialized coding: the ability to apply AI, solve complex problems, exercise judgment and work effectively with other people.

In 2017, I argued that the critical skills needed were:

• Problem solving and critical thinking: particularly evaluating information, identifying errors, asking good questions, and exercising judgment. AI arguably increases the return to these abilities because generating an answer becomes cheap while determining whether it is a good answer remains difficult

• Learning and adaptability: the capacity to acquire new skills as technology changes tasks and occupations

• Communication: reading, writing, explaining, persuading, and interpreting information. AI makes strong communication skills more, not less, valuable

• Self-management: persistence, reliability, initiative, judgment, and the ability to work independently

• Social and interpersonal skills: teamwork, leadership, negotiation, empathy, management

To this list I would add:

· Foundational skills: literacy, numeracy, and increasingly digital/AI literacy. These are prerequisites rather than competitors to higher-order skills.

The technology has changed dramatically. The skills that complement it have changed much less.

The evidence for such skills has grown considerably over time. But recognizing which skills matter is easier than reforming education systems to produce them. To make the challenge even more profound, many students aren’t getting the basic skills early on. I don’t mean coding and complex problem solving; I mean reading and math. An estimated 70 percent of 10-year-olds in low- and middle-income countries cannot read and understand a simple text. And this is not just a developing-country problem: one in four 15-year-olds across OECD countries is a low performer in mathematics, reading and science. At precisely the moment when workers need sophisticated problem-solving, adaptability, and judgment, education systems are still struggling to ensure that children master the basics.

So, what did we recommend in 2017 – and would I change it today?”:

1. Assessment: “Countries that are unable to determine where their education system stands currently will find it difficult to make improvements or to reach their goals.” That remains true, but assessment should tell us not only whether students possess foundational knowledge but whether they can apply it. Mississippi’s literacy reforms offer a useful example: assessment identifies struggling readers early enough for schools to intervene rather than waiting until failure becomes entrenched.

2. Autonomy: “Empowering schools will support quality improvements.” Technology changes too rapidly for ministries or state departments to prescribe every instructional response centrally. Give schools and teachers room to experiment – but within a framework of measured outcomes and accountability. And we should distinguish between unstructured technology use and purposeful instructional use. Phones can be distracting while AI can simultaneously be a powerful instructional tool. Estonia, one of the world’s highest-performing education systems, has long combined strong foundational learning with extensive use of digital technology.

3. Accountability: “Accountability increases time on task and academic achievement.” Autonomy without accountability risks becoming permission to fail students. Accountability without autonomy risks becoming compliance. Accountability also requires information. Students need information about what skills employers value, what education pathways lead to which outcomes, and what different credentials actually pay. Schools need information about student learning. Teachers need information about what works. Policymakers need information about outcomes.

4. Attention to teachers: “…a good teacher…can be effective in helping students to improve their learning out­comes.” AI increases rather than eliminates the importance of excellent teachers because teachers determine how technology is used. Singapore is a natural example because of selective recruitment, training, career development, and professionalization.

5. Attention to early childhood development (ECD): if later adaptability depends on foundational cognitive and socioemotional skills, we cannot wait until workers enter the labor market to develop them. The race between education and technology starts early.

6. Attention to personalization: Students have different learning needs and different starting points. Education needs to meet students where they are. Doing that individually was prohibitively expensive in the past. Technology makes it increasingly possible. AI potentially changes the production technology of education itself. Instead of designing schooling around an imaginary average student, technology can help diagnose what individual students know, adjust difficulty, provide immediate feedback, translate material, and give teachers information about who needs help. But personalization should be teacher-enabled rather than technology-led.

Nine years later, I would change some of the details of what I wrote, but not the basic prescription. Education systems need to know whether students are learning. Schools and teachers need enough autonomy to adapt as technology changes. That autonomy needs to be accompanied by accountability for results. We need excellent teachers, strong early-childhood development, and much greater capacity to personalize learning.

Above all, we need to stop treating the skills of the future as something separate from the skills students should already be learning. Reading, mathematics, critical thinking, communication, adaptability, judgment, and the ability to work with others are not old-fashioned skills made obsolete by AI. They are precisely the skills that allow people to use AI rather than be displaced by it.

The robots did come. Now AI has arrived too. The question is no longer whether technology will change work. It is whether education can change fast enough to keep up.

A follow up to a blog published on March 1, 2017, and a paper under the same title as the blog for the Brookings Blum Roundtable.

I am m pleased to share that my new paper (with Angelica Rivera-Olvera) “Education and Earnings in Arkansas” has been published in the Journal of Economic Insight.

The paper provides the first recent, comprehensive analysis of the economic returns to education in Arkansas using 2024 Current Population Survey data. We estimate returns using multiple methods - including Mincer earnings functions, full-discounting, Heckman selection correction, Oaxaca-Blinder decomposition, and quantile regression - and find that education continues to be a strong investment. Each additional year of schooling is associated with about a 7.5% increase in earnings, with even higher returns for women. We also document an intriguing post-pandemic decline in Arkansas’s returns to education that contrasts with national trends.

You can read the published version here:
https://journalofeconomicinsight.com/index.php/joei/article/view/1425

An open version is also available on my website:
https://hpatrinos.com/wp-content/uploads/2026/08/patrinos_rivera-olvera.pdf

This is especially important because educational attainment in Arkansas has increased substantially over time. Despite this expansion in the supply of educated workers, the labor market continues to reward additional schooling. Each additional year of education is associated with an average 7.5 percent increase in earnings, rising to 8.6 percent for women. University education pays even more, with estimated returns of 8.9 percent overall and 10.8 percent for women. Using a full-discounting approach, we estimate a private return to university education of 10.3 percent.

There is one warning sign: returns to education in Arkansas have declined since the pandemic, in contrast to national trends. Nevertheless, education still pays—and pays well. Expanding access to high-quality postsecondary education can raise earnings, strengthen Arkansas’s workforce, and contribute to the state’s long-term economic growth.

Education and Earnings in Arkansas -- This paper presents the first recent systematic analysis of the returns to education in Arkansas using 2024 Current Population Survey data. Across methods (Mincerian discounting methods, Heckman’s two earnings function and full step procedure, Binder-Oaxaca, and quantile regression), education remains a profitable investment, with average returns of 7.5 percent per additional year of schooling and higher returns for women (8.6 percent) than for men (7.5 percent). University education yields even greater rewards — 8.9 percent overall, 8.1 percent for men, and 10.8 percent for women — with full discounting estimates placing private returns at 10.3 percent and “social” returns at 5.6 percent. Unlike national trends, Arkansas’s returns have declined since the pandemic, a shift that could dampen incentives for further study. However, the strong and consistent returns, especially for women, suggest that expanding access to and quality of higher education can be a powerful driver of earnings growth, workforce development, and long term economic prosperity. (Patrinos HA, Rivera Olvera A (2026). Education and Earnings in Arkansas. Journal of Economic Insight 52(1), forthcoming).

For years, the returns to education in the United States had been quietly declining. Then the pandemic hit, and the trend reversed sharply. When millions of workers faced unprecedented labor market disruption, the economic value of education surged.

The gains were not evenly distributed. University-educated men saw the largest increases — returns to university education rose by more than 5 percentage points during and after the pandemic. Meanwhile, less-educated workers faced substantially higher unemployment and weaker earnings growth.

The most consequential finding may be this: COVID-19 didn’t just change who earned more — it widened the economic divide between higher- and lower-skilled workers. Education didn’t simply boost earnings; it functioned as a form of protection against one of the largest economic shocks in recent memory.

The lesson is not new, but the pandemic made it impossible to ignore: education remains one of the strongest investments a person can make. In times of crisis, that value may be even greater than in stable times.

Full paper: The returns to education over time and the effect of COVID-19

Harry Anthony Patrinos & Angelica Rivera-Olvera (2026), Applied Economics, 58:28, 5432–5444. DOI: 10.1080/00036846.2025.2506845

Education remains one of the best investments individuals and societies can make. The evidence is remarkably consistent: higher levels of education are associated with higher earnings, better employment opportunities, and stronger economic growth. As George Psacharopoulos and I have argued for many years, the returns to education remain high across countries and over time — reinforcing the case for investing not only in access to schooling, but also in learning and skills.

Read the chapter here: https://link.springer.com/rwe/10.1007/978-3-319-57365-6_489-1

Reference: Patrinos, H.A., Psacharopoulos, G. (2026). Seventy Years of Human Capital: What We Know and What We Still Need to Learn. In: Zimmermann, K.F. (eds) Handbook of Labor, Human Resources and Population Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-57365-6_489-1

A new review of global evidence suggests the answer is clear. Learning matters enormously - and perhaps even more than years of schooling alone. Drawing on 73 studies from 41 countries, George Psacharopoulos and I find that a one standard deviation increase in student achievement is associated with about a 15 percent increase in earnings later in life.

For comparison, the average return to an additional year of schooling worldwide is about 10 percent. In other words, improving what students learn may generate economic benefits that are at least as large as expanding the quantity of schooling.

Historically, education policy focused on access. The challenge was getting children into school. That was the right priority for much of the twentieth century. Today, however, many countries have achieved near-universal enrollment. Yet millions of students complete primary or even secondary school without mastering basic literacy and numeracy skills. This has shifted the policy conversation from schooling to learning. The distinction matters.

Years of schooling are easy to count. Learning is harder to measure. But from an economic perspective, employers reward skills, not seat time. The labor market values what workers can do.

The Evidence Is Remarkably Consistent

The studies reviewed in our paper use a variety of methods. Some exploit admission cutoffs for selective schools. Others use natural experiments, longitudinal datasets, cognitive assessments, or twin studies. Despite the differences in methods and contexts, the results point in the same direction: stronger cognitive skills are associated with higher earnings.

What is particularly striking is that the relationship holds across countries at different income levels. The earnings premium associated with better learning outcomes is roughly the same in high-income countries as it is in middle-income countries. Improving learning appears to be universally valuable.

Why Skills Matter

One reason is that schooling affects earnings largely through skill acquisition. Several studies show that when direct measures of cognitive skills are included in earnings equations, the estimated return to years of schooling falls. In other words, part of what we have traditionally attributed to schooling is actually the result of learning.

The message for policymakers is straightforward. Expanding access remains important where children are still excluded from education. But enrollment alone is not enough. Countries should pay equal attention to what students learn while they are in school.

This means investing in interventions that improve instructional quality, strengthen literacy and numeracy, support teachers, and ensure students master foundational skills. It means measuring success not only by enrollment rates and years of schooling, but also by learning outcomes.

In a recent paper published in The Review of Economics and Statistics, with Noam Angrist, Kevin Winseck, and Joshua Graff Zivin, we take this question seriously.

Using variation from compulsory schooling reforms across 20 European countries, we identify the causal impact of additional schooling on a range of pro-climate outcomes.

The results are clear:

· An additional year of schooling increases pro-climate beliefs

· It leads to more environmentally responsible behaviors

· It strengthens support for climate policies

· It increases the likelihood of voting for green parties

This is important.

Climate change is not only a technological or regulatory challenge – it is also a behavioral and political one. Achieving meaningful progress depends on informed citizens, public support for policy, and sustained civic engagement.

Education appears to shift all three.

From a human capital perspective, this adds another dimension to the returns to schooling. Education does not just raise earnings or productivity – it shapes preferences, information processing, and participation in collective decision-making. These are central to addressing global public goods like climate change.

The implication for policy is straightforward: investments in education can complement traditional climate policies by building the societal foundations for long-run action.

Read the paper.

But what does the broader literature say?

In a new review with Diego Ambasz and Anshuman Gupta, we step back and assess the full body of evidence linking human development – especially schooling – to environmental outcomes.

The headline finding is striking – out of 31 studies:

· 27 are observational

· Only 4 use quasi-experimental methods to identify causal effects

That imbalance matters. Much of what we think we know about education and climate outcomes rests on correlations, not causal inference.

Substantively, the literature points to a consistent but incomplete pattern:

· Education is strongly associated with pro-climate attitudes

· Translating those attitudes into actual behavioral change is much harder

This gap – between beliefs and action – is central to climate policy.

To organize the evidence, we propose a simple framework:

· Cognitive channels (knowledge, information)

· Affective channels (values, preferences)

· Situational constraints (income, context, incentives)

Education clearly operates through the first two. But behavior is often constrained by the third. The small number of causal studies suggests that schooling can influence behavior. But the evidence is still thin, and the mechanisms are not fully understood.

This leads to a more cautious conclusion:

If education is to play a meaningful role in climate mitigation and adaptation, research – and policy – must move beyond correlations and focus on causal pathways and real behavioral outcomes.

There is still a great deal we do not know.

Read the review.

SustainableED

I will be discussing this work at the SustainableED 2026 Conference at the Annenberg Institute at Brown University on May 1, 2026, where I will present A Review of Human Development and Environmental Outcomes.

Initial findings were presented at the Association for Education Finance and Policy (AEFP), American Educational Research Association (AERA), and Harvard University under the title “Designing Effective Public-Private Partnerships in Education.”

Presentation available: https://hpatrinos.com/2026/04/17/designing-effective-ppps-in-educationdoes-governance-design-matter-more-than-private-provision/

The paper conceptualizes publicly funded school choice as a form of education public-private partnership (PPP) and evaluates whether cross-national differences in performance are driven by institutional design rather than private provision per se. Using structured comparative case analysis and policy benchmarking data, six countries and the fragmented U.S. system are evaluated across four core dimensions: competition, autonomy, accountability, and information.

Two composite indicators – alignment (overall policy development) and coherence (consistency across domains) – are constructed to capture system configuration. Preliminary results indicate that higher-performing systems exhibit both stronger alignment and greater internal coherence, whereas fragmented configurations are associated with more variable outcomes. While descriptive, the analysis identifies systematic patterns consistent with theories of policy complementarity in governance.

1. Using AI to Improve the Quality of Education

Artificial intelligence (AI) presents both a transformative opportunity and a profound challenge. On the one hand, AI can help address chronic constraints (large classes, shortages of qualified teachers, and limited access to high-quality learning materials) by personalizing instruction, supporting formative assessment, and assisting teachers with lesson planning and feedback. Adaptive learning systems can tailor content to individual student needs, helping close learning gaps more efficiently than traditional one-size-fits-all approaches. On the other hand, without thoughtful integration, AI risks widening inequalities. Schools with connectivity, devices, and trained staff may benefit, while marginalized communities fall further behind. Moreover, governance frameworks for data protection, ethical use, and quality assurance are often underdeveloped. If education systems fail to proactively shape how AI is adopted – investing in infrastructure, teacher training, and regulatory safeguards – they risk becoming passive consumers of external technologies that may not align with national curricula, languages, or cultural contexts. Therefore, the way systems engage with AI today will significantly influence both equity and learning outcomes in the years ahead.

2. Failure to Achieve Foundational Literacy at the Right Age

The inability of many children to read with comprehension by the end of the early primary grades represents a foundational crisis – and one that is deeply unequal. This challenge not only differs sharply between low-, middle-, and high-income countries, but also within them, disproportionately affecting disadvantaged populations. The inequity dimension permeates all four themes: gaps in access, quality, resources, and learning conditions reinforce one another, widening disparities at every stage of the system. Literacy is not merely another subject; it is the gateway skill upon which nearly all subsequent learning depends. When students do not master reading early, they struggle to access content in mathematics, science, and other disciplines, leading to cumulative learning deficits that become increasingly difficult and costly to remediate. Research consistently shows that early grade reading proficiency strongly predicts later academic achievement, school completion rates, and even long-term economic prospects. In low- and low/middle-income contexts, factors such as overcrowded classrooms, instruction in languages children do not speak at home, insufficient learning materials, and limited early childhood education exacerbate the problem. The long-term societal implications are severe: cohorts of youth may leave school functionally illiterate, constraining workforce productivity, civic participation, and national development. Addressing foundational literacy is therefore not only an educational priority but also a social and economic imperative.

3. Inadequate Supply of High-Quality Teachers

Teachers are the most influential school-based factor affecting student learning. Yet many countries face acute shortages of qualified educators, particularly in rural and disadvantaged areas. Even where teacher numbers are expanding, preparation and ongoing professional development often lag behind. This happens more in low-income countries. In some systems, teachers enter classrooms with limited subject mastery, insufficient pedagogical training, and minimal mentoring support. High absenteeism, low morale, and weak accountability structures can further erode effectiveness. Without a strong teaching workforce, investments in curriculum reform, technology, or infrastructure are unlikely to yield meaningful improvements in learning outcomes. Furthermore, inequitable teacher distribution means that the most vulnerable students are often taught by the least experienced or least prepared staff, reinforcing cycles of disadvantage. Strengthening teacher recruitment, preparation, support, and career pathways is therefore central to improving both quality and equity in education systems.

4. Weak or Nonexistent Accountability

Effective governance underpins all other reforms. When governance structures are weak – characterized by unclear roles and responsibilities, limited accountability, insufficient data systems, and vulnerability to political interference – education systems struggle to translate policy into practice. Poor financial management can lead to misallocation of scarce resources, while weak monitoring and evaluation hinder evidence-based decision-making. In some contexts, decentralization has occurred without adequate capacity at local levels, resulting in inconsistent implementation and oversight, and inequity in the distribution of education outcomes. Corruption, patronage-based hiring, and opaque procurement processes can further undermine system integrity. Without strong governance, even well-designed reforms in literacy, teacher development, or technology integration may fail to scale or sustain impact. Robust accountability frameworks (grounded in transparency, accountability, reliable data, and stakeholder engagement) are essential to ensure that resources are used effectively and that reforms genuinely improve learning for all students.

What struck us most in these conversations is not that these problems persist, but that we now understand them better than ever. We know what works: early literacy, strong teachers, accountability, and increasingly, the careful use of technology. The challenge is not diagnosis; rather, it is implementation, at scale, and with equality of opportunity at the center. The systems that succeed will be those that treat learning not as an average outcome, but as a distribution, and commit to lifting the bottom as much as the top.

********************

Eduardo Velez is former World Bank Education Sector Manager for East Asia and the Pacific and for Latin American and the Caribbean, and many other posts. He has held teaching positions at Georgetown University, Kobe Universitym, and Peking University. Has published ten books and 100 articles in professional journals and research reports.

In my new The 74 analysis, I make a simple, evidence-based point: retention by itself won’t raise literacy. What moved outcomes in places like Mississippi (and what matters in other states’ evidence too) is the full package—early identification, structured interventions, tutoring/coaching, and sustained support before and after any promotion decision. Retention can be a policy lever, but the supports are the mechanism.

If you’re tracking science-of-reading reforms, promotion gates, and what it takes to make them work in practice, this is the core takeaway: don’t debate retention in isolation—debate the capacity and resourcing around it.

Read the piece here: Arkansas Will Soon Hold Back Kids Who Can’t Read. But That Alone Is Not Enough (The 74, Feb. 19, 2026).

Yet today, public debate often suggests the opposite. We hear that college is no longer worth it, that schooling has lost value, or that education systems produce credentials but not skills. Policymakers face a real dilemma: how much should governments invest in education, and where should those resources go?

Answering those questions requires measurement — not anecdotes.

This week we launched the Human Capital and Education Returns (HCER) Lab at the University of Arkansas Department of Education Reform. The lab’s purpose is straightforward: to measure the economic returns to education clearly, rigorously, and in ways policymakers can actually use.

Learn more: HCER

What we study

At its core, the lab focuses on a central economic question:

What is the payoff to learning?

We estimate both private and social returns to education — how schooling affects wages, employment, productivity, growth, and mobility — using causal methods and large-scale datasets from the United States and around the world.

Our work combines:

• U.S. administrative and survey data

• International assessments and global datasets

• Natural experiments and policy reforms

• Comparative policy analysis across countries and states

The objective is not just academic precision. It is policy clarity.

Governments routinely make multi-billion-dollar education decisions without clear estimates of economic impact. We aim to change that.

Why this matters now

Three major developments make measurement more important than ever.

1. Post-pandemic learning losses
Students lost years of skills. The real policy question is not only how much learning declined — but what that means for lifetime earnings and national productivity.

2. The rise of AI and skill-biased change
Technological change is increasing the value of cognitive and adaptable skills. Education systems must know which investments generate real human capital.

3. Fiscal pressure
Finance ministries increasingly ask: which education policies deliver measurable economic returns? Education is no longer funded on good intentions alone.

Our research agenda

The HCER Lab focuses on evidence that directly informs policy decisions:

• Returns to education in Arkansas and the United States

• Global causal returns to schooling

• Long-term economic impacts of learning loss

• Human capital measurement and benchmarking

• Education finance and public-private partnerships

• Skills, technology, and productivity

We are fortunate to have economist George Psacharopoulos as advisor and a strong research team including Temurbek Rakhmatov, Xueshen Shi, Angelica Rivera, and Kristen Scott.

A simple idea

The economics of education is ultimately empirical. Education has always been a powerful investment. Our task is to quantify it — and make the evidence usable.

If you are working on related topics — policy evaluation, education finance, labor markets, or human capital — we welcome collaboration.

More information: HCER

This month’s spotlight features new research on the returns to education in Arkansas, updates on teacher pipeline initiatives, evidence-based policy work with schools and state leaders, and upcoming lectures and events. We also share recent studies, expert commentary, and stories of schools and educators improving outcomes for students.

📬 Read the full newsletter and subscribe for future updates: https://shorturl.at/sUaae

Thank you for staying connected to our work.

Arkansas a model for evidence-based education reform: Arkansas a proving ground for reform

Twenty years ago, the University of Arkansas launched the Department of Education Reform (EDRE) with the bold vision of using research to transform how education policy is designed and delivered. Two decades later, Arkansas has become a national proving ground for what is possible when evidence, policy and community align for students.

The Times of India recently reported, “Arkansas has become one of America’s most consequential education experiments.” For the world to be taking note of the innovation in Arkansas shows just how far the state has come and how much it has to teach the rest of the country.

The timing couldn’t be more crucial. The latest National Assessment of Educational Progress (NAEP) results show that high school students’ reading and math scores are the lowest in over 20 years, signaling a national education crisis with severe economic consequences. This signals not just an education crisis, but an economic one. These students represent the future workforce, and as an economist, these results alarm me. Decades of research show that lower academic achievement translates into slower economic growth. Less schooling means lower future earnings. When our students struggle, our state and our nation feel the consequences.

Yet amid these challenges, Arkansas offers a blueprint for hope. Through evidence-based policymaking and bold reforms, Arkansas has embraced reforms like the 2023 LEARNS Act. Arkansas has raised teacher pay, expanded literacy coaching and empowered families with school choice.

A key driver of Arkansas’ reform efforts has been the EDRE, which has supported the state’s education transformation by producing rigorous, policy-focused research highlighting the effects of initiatives like school choice and teacher workforce improvements.

Teacher workforce research from EDRE informed the design of key components of the LEARNS Act, directly shaping initiatives that raised the minimum teacher salary to $50,000 and introduced targeted strategies to improve early literacy and expand school choice. EDRE’s analysis of teacher turnover and administrator perspectives has provided policymakers with critical insights into workforce stability and the real-world impact of reform.

This is what reform looks like when it is grounded in evidence and backed by research. Choice inspires improvement. It drives schools to innovate, engage, and meet families where they are. Combined with teacher reforms and early literacy programs--like ensuring every child can read by Grade 3--Arkansas is building a system that is both ambitious and inclusive.

Beyond that, our researchers have addressed chronic absenteeism, English-language learning, grading practices, and teacher turnover during covid-19. We’ve done this not from an ivory tower, but with the conviction that research should serve students, parents, teachers and communities.

Last month, EDRE celebrated its 20th anniversary with a two-day conference, “Education Reform Then and Now: A Policy Conference,” held at the Crystal Bridges Museum of American Art in Bentonville. The event brought together educators, policymakers, researchers, and advocates from across the country underscoring Arkansas’ growing prominence as a hub for national education dialogue.

The story unfolding in Arkansas is bigger than our state; it’s about what’s possible when research and action are in partnership. The next 20 years will test our resolve as a nation, and Arkansas is showing how to lead with courage, evidence and impact.

We must stay focused on what works--data-driven, equitable, and student-centered reform. That is how we ensure our next generation of learners inherit a legacy of progress. Arkansas stands at the center of national attention, not just for what has been done, but for what is possible to be achieved.

https://www.nwaonline.com/news/2025/nov/17/arkansas-a-model-for-evidence-based-education/