Modernizing AI Fairness Analysis in Education Contexts

The 2022 release of ChatGPT and subsequent foundation models sparked a generative AI (GenAI) explosion in American society, driving rapid adoption of AI-powered tools in schools, colleges, and universities nationwide. Education technology was one of the first applications used to develop and test ChatGPT in a real-world context. A recent national survey indicated that nearly 50% of teachers, students, and parents use GenAI Chatbots in school, and over 66% of parents and teachers believe that GenAI Chatbots can help students learn more and faster. While this innovation is exciting and holds tremendous promise to personalize education, educators, families, and researchers are concerned that AI-powered solutions may not be equally useful, accurate, and effective for all students, in particular students from minoritized populations. It is possible that as this technology further develops that bias will be addressed; however, to ensure that students are not harmed as these tools become more widespread it is critical for the Department of Education to provide guidance for education decision-makers to evaluate AI solutions during procurement, to support EdTech developers to detect and mitigate bias in their applications, and to develop new fairness methods to ensure that these solutions serve the students with the most to gain from our educational systems. Creating this guidance will require leadership from the Department of Education to declare this issue as a priority and to resource an independent organization with the expertise needed to deliver these services.  

Challenge and Opportunity

Known Bias and  Potential Harm

There are many examples of the use of AI-based systems introducing more bias into an already-biased system. One example with widely varying results for different student groups is the use of GenAI tools to detect AI-generated text as a form of plagiarism. Liang et. al  found that several GPT-based plagiarism checkers frequently identified the writing of students for whom English is not their first language as AI-generated, even though their work was written before ChatGPT was available. The same errors did not occur with text generated by native English speakers. However, in a publication by Jiang (2024), no bias against non-native English speakers was encountered in the detection of plagiarism between human-authored essays and ChatGPT-generated essays written in response to analytical writing prompts from the GRE, which is an example of how thoughtful AI tool design and representative sampling in the training set can achieve fairer outcomes and mitigate bias. 

Beyond bias, researchers have raised additional concerns about the overall efficacy of these tools for all students; however, more understanding around different results for subpopulations and potential instances of bias(es) is a critical aspect of deciding whether or not these tools should be used by teachers in classrooms. For AI-based tools to be usable in high-stakes educational contexts such as testing, detecting and mitigating bias is critical, particularly when the consequences of being incorrect are so high, such as for students from minoritized populations who may not have the resources to recover from an error (e.g., failing a course, being prevented from graduating school). 

Another example of algorithmic bias before the widespread emergence of GenAI which illustrates potential harms is found in the Wisconsin Dropout Early Warning System. This AI-based tool was designed to flag students who may be at risk of dropping out of school; however, an analysis of the outcomes of these predictions found that the system disproportionately flagged African American and Hispanic students as being likely to drop out of school when most of these students were not at risk of dropping out). When teachers learn that one of their students is at risk, this may change how they approach that student, which can cause further negative treatment and consequences for that student, creating a self-fulfilling prophecy and not providing that student with the education opportunities and confidence that they deserve. These examples are only two of many consequences of using systems that have underlying bias and demonstrate the criticality of conducting fairness analysis before these systems are used with actual students. 

Existing Guidance on Fair AI & Standards for Education Technology Applications

Guidance for Education Technology Applications

Given the harms that algorithmic bias can cause in educational settings, there is an opportunity to provide national guidelines and best practices that help educators avoid these harms. The Department of Education is already responsible for protecting student privacy and provides guidelines via the Every Student Succeeds Act (ESSA) Evidence Levels to evaluate the quality of EdTech solution evidence. The Office of Educational Technology, through support of a private non-profit organization (Digital Promise) has developed guidance documents for teachers and administrators, and another for education technology developers (U.S. Department of Education, 2023, 2024). In particular, “Designing for Education with Artificial Intelligence” includes guidance for EdTech developers including an entire section called “Advancing Equity and Protecting Civil Rights” that describes algorithmic bias and suggests that, “Developers should proactively and continuously test AI products or services in education to mitigate the risk of algorithmic discrimination.” (p 28). While this is a good overall guideline, the document critically is not sufficient to help developers conduct these tests. 

Similarly, the National Institute of Standards and Technology has released a publication on identifying and managing bias in AI . While this publication highlights some areas of the development process and several fairness metrics, it does not provide specific guidelines to use these fairness metrics, nor is it exhaustive. Finally demonstrating the interest of industry partners, the EDSAFE AI Alliance, a philanthropically-funded alliance representing a diverse group of companies in educational technology, has also created guidance in the form of the 2024 SAFE (Safety, Accountability, Fairness, and Efficacy) Framework. Within the Fairness section of the framework, the authors highlight the importance of using fair training data, monitoring for bias, and ensuring accessibility of any AI-based tool. But again, this framework does not provide specific actions that education administrators, teachers, or EdTech developers can take to ensure these tools are fair and are not biased against specific populations. The risk to these populations and existing efforts demonstrate the need for further work to develop new approaches that can be used in the field. 

Fairness in Education Measurement

As AI is becoming increasingly used in education, the field of educational measurement has begun creating a set of analytic approaches for finding examples of algorithmic bias, many of which are based on existing approaches to uncovering bias in educational testing. One common tool is called Differential Item Functioning (DIF), which checks that test questions are fair for all students regardless of their background. For example, it ensures that native English speakers and students learning English have an equal chance to succeed on a question if they have the same level of knowledge . When differences are found, this indicates that a student’s performance on that question is not based on their knowledge of the content. 

While DIF checks have been used for several decades as a best practice in standardized testing, a comparable process in the use of AI for assessment purposes does not yet exist. There also is little historical precedent indicating that for-profit educational companies will self-govern and self-regulate without a larger set of guidelines and expectations from a governing body, such as the federal government. 

We are at a critical juncture as school districts begin adopting AI tools with minimal guidance or guardrails, and all signs point to an increase of AI in education. The US Department of Education has an opportunity to take a proactive approach to ensuring AI fairness through strategic programs of support for school leadership, developers in educational technology, and experts in the field. It is important for the larger federal government to support all educational stakeholders under a common vision for AI fairness while the field is still at the relative beginning of being adopted for educational use. 

Plan of Action 

To address this situation, the Department of Education’s Office of the Chief Data Officer should lead development of a national resource that provides direct technical assistance to school leadership, supports software developers and vendors of AI tools in creating quality tech, and invests resources to create solutions that can be used by both school leaders and application developers. This office is already responsible for data management and asset policies, and provides resources on grants and artificial intelligence for the field. The implementation of these resources would likely be carried out via grants to external actors with sufficient technical expertise, given the rapid pace of innovation in the private and academic research sectors. Leading the effort from this office ensures that these advances are answering the most important questions and can integrate them into policy standards and requirements for education solutions. Congress should allocate additional funding to the Department of Education to support the development of a technical assistance program for school districts, establish new grants for fairness evaluation tools that span the full development lifecycle, and pursue an R&D agenda for AI fairness in education. While it is hard to provide an exact estimate, similar existing programs currently cost the Department of Education between $4 and $30 million a year. 

Action 1. The Department of Education Should Provide Independent Support for School Leadership Through a Fair AI Technical Assistance Center (FAIR-AI-TAC) 

School administrators are hearing about the promise and concerns of AI solutions in the popular press, from parents, and from students. They are also being bombarded by education technology providers with new applications of AI within existing tools and through new solutions. 

These busy school leaders do not have time to learn the details of AI and bias analysis, nor do they have the technical background required to conduct deep technical evaluations of fairness within AI applications. Leaders are forced to either reject these innovations or implement them and expose their students to significant potential risk with the promise of improved learning. This is not an acceptable status quo.  

To address these issues, the Department of Education should create an AI Technical Assistance Center (the Center) that is tasked with providing direct guidance to state and local education leaders who want to incorporate AI tools fairly and effectively. The Center should be staffed by a team of professionals with expertise in data science, data safety, ethics, education, and AI system evaluation. Additionally, the Center should operate independently of AI tool vendors to maintain objectivity.

There is precedent for this type of technical support. The U.S. Department of Education’s Privacy Technical Assistance Center (PTAC) provides guidance related to data privacy and security procedures and processes to meet FERPA guidelines; they operate a help desk via phone or email, develop training materials for broad use, and provide targeted training and technical assistance for leaders. A similar kind of center could be stood up to support leaders in education who need support evaluating proposed policy or procurement decisions.  

This Center should provide a structured consulting service offering a variety of levels of expertise based on the individual stakeholder’s needs and the variety of levels of potential impact of the system/tool being evaluated on learners; this should include everything from basic levels of AI literacy to active support in choosing technological solutions for educational purposes. The Center should partner with external organizations to develop a certification system for high-quality AI educational tools that have passed a series of fairness checks. Creating a fairness certification (operationalized by third party evaluators)  would make it much easier for school leaders to recognize and adopt fair AI solutions that meet student needs. 

Action 2. The Department of Education Should Provide Expert Services, Data, and Grants for EdTech Developers 

There are many educational technology developers with AI-powered innovations. Even when well-intentioned, some of these tools do not achieve their desired impacts or may be unintentionally unsafe due to a lack of processes and tests for fairness and safety.

Educational Technology developers generally operate under significant constraints when incorporating AI models into their tools and applications. Student data is often highly detailed and deeply personal, potentially containing financial, disability, and educational status information that is currently protected by FERPA, which makes it unavailable for use in AI model training or testing. 

Developers need safe, legal, and quality datasets that they can use for testing for bias, as well as appropriate bias evaluation tools. There are several promising examples of these types of applications and new approaches to data security, such as the recently awarded NSF SafeInsights project, which allows analysis without disclosing the underlying data. In addition, philanthropically-funded organizations such as the Allen Institute for AI have released LLM evaluation tools that could be adapted and provided to Education Technology developers for testing. A vetted set of evaluation tools, along with more detailed technical resources and instructions for how to use them would encourage developers to incorporate bias evaluations early and often. Currently, there are very few market incentives or existing requirements that push developers to invest the necessary time or resources into this type of fairness analysis. Thus, the government has a key role to play here.

The Department of Education should also fund a new grant program that tasks grantees with developing a robust and independently validated third-party evaluation system that checks for fairness violations and biases throughout the model development process from pre-processing of data, to the actual AI use, to testing after AI results are created. This approach would support developers in ensuring that the tools they are publishing meet an agreed-upon minimum threshold for safe and fair use and could provide additional justification for the adoption of AI tools by school administrators.

Action 3. The Department of Education Should Develop Better Fairness R&D Tools with Researchers 

There is still no consensus on best practices for how to ensure that AI tools are fair. As AI capabilities evolve, the field needs an ongoing vetted set of analyses and approaches that will ensure that any tools being used in an educational context are safe and fair for use with no unintended consequences.

The Department of Education should lead the creation of a a working group or task force comprised of subject matter experts from education, educational technology, educational measurement, and the larger AI field to identify the state of the art in existing fairness approaches for education technology and assessment applications, with a focus on modernized conceptions of identity. This proposed task force would be an inter-organizational group that would include representatives from several different federal government offices, such as the Office of Educational Technology and the Chief Data Office as well as prominent experts from industry and academia. An initial convening could be conducted alongside leading national conferences that already attract thousands of attendees conducting cutting-edge education research (such as the American Education Research Association and National Council for Measurement in Education).

The working group’s mandate should include creating a set of recommendations for federal funding to advance research on evaluating AI educational tools for fairness and efficacy. This research agenda would likely span multiple agencies including NIST, the Institute of Education Sciences of the U.S. Department of Education, and the National Science Foundation. There are existing models for funding early stage research and development with applied approaches, including the IES “Accelerate, Transform, Scale” programs that integrate learning sciences theory with efforts to scale theories through applied education technology program and Generative AI research centers that have the existing infrastructure and mandates to conduct this type of applied research. 

Additionally, the working group should recommend the selection of a specialized group of researchers who would contribute ongoing research into new empirically-based approaches to AI fairness that would continue to be used by the larger field. This innovative work might look like developing new datasets that deliberately look for instances of bias and stereotypes, such as the CrowS-Pairs dataset. It may build on current cutting edge research into the specific contributions of variables and elements of LLM models that directly contribute to biased AI scores, such as the work being done by the AI company Anthropic. It may compare different foundation LLMs and demonstrate specific areas of bias within their output. It may also look like a collaborative effort between organizations, such as the development of the RSM-Tool, which looks for biased scoring. Finally, it may be an improved auditing tool for any portion of the model development pipeline. In general, the field does not yet have a set of universally agreed upon actionable tools and approaches that can be used across contexts and applications; this research team would help create these for the field.

Finally, the working group should recommend policies and standards that would incentivize vendors and developers working on AI education tools to adopt fairness evaluations and share their results.

Conclusion

As AI-based tools continue being used for educational purposes, there is an urgent need to develop new approaches to evaluating these solutions to fairness that include modern conceptions of student belonging and identity. This effort should be led by the Department of Education, through the Office of the Chief Data Officer, given the technical nature of the services and the relationship with sensitive data sources. While the Chief Data Officer should provide direction and leadership for the project, partnering with external organizations through federal grant processes would provide necessary capacity boosts to fulfill the mandate described in this memo.As we move into an age of widespread AI adoption, AI tools for education will be increasingly used in classrooms and in homes. Thus, it is imperative that robust fairness approaches are deployed before a new tool is used in order to protect our students, and also to protect the developers and administrators from potential litigation, loss of reputation, and other negative outcomes.

This action-ready policy memo is part of Day One 2025 — our effort to bring forward bold policy ideas, grounded in science and evidence, that can tackle the country’s biggest challenges and bring us closer to the prosperous, equitable and safe future that we all hope for whoever takes office in 2025 and beyond.

Frequently Asked Questions
What are some examples of what is currently being done to ensure fairness in AI applications for educational purposes?

When AI is used to grade student work, fairness is evaluated by comparing the scores assigned by AI to those assigned by human graders across different demographic groups. This is often done using statistical metrics, such as the standardized mean difference (SMD), to detect any additional bias introduced by the AI. A common benchmark for SMD is 0.15, which suggests the presence of potential machine bias compared to human scores. However, there is a need for more guidance on how to address cases where SMD values exceed this threshold.


In addition to SMD, other metrics like exact agreement, exact + adjacent agreement, correlation, and Quadratic Weighted Kappa are often used to assess the consistency and alignment between human and AI-generated scores. While these methods provide valuable insights, further research is needed to ensure these metrics are robust, resistant to manipulation, and appropriately tailored to specific use cases, data types, and varying levels of importance.

What are some concerns about using AI in education for students with diverse and overlapping identities?

Existing approaches to demographic post hoc analysis of fairness assume that there are two discrete populations that can be compared, for example students from African-American families vs. those not from African-American families, students from an English language learner family background vs. those that are not, and other known family characteristics. However in practice, people do not experience these discrete identities. Since at least the 1980s, contemporary sociological theories have emphasized that a person’s identity is contextual, hybrid, and fluid/changing. One current approach to identity that integrates concerns of equity that has been applied to AI is “intersectional identity” theory . This approach has begun to develop promising new methods that bring contemporary approaches to identity into evaluating fairness of AI using automated methods. Measuring all interactions between variables results in too small a sample; these interactions can be prioritized using theory or design principles or more advanced statistical techniques (e.g., dimensional data reduction techniques).

Using Title 1 to Unlock Equity-Focused Innovation for Students

Congress should approve a new allowable use of Title I spending that specifically enables and encourages school districts to use funds for activities that support and drive equity-focused innovation. The persistent equity gap between wealthy and poor students in our country, and the continuing challenges caused by the pandemic, demand new, more effective strategies to help the students who are most underserved by our public education system.

Efforts focused on the distribution of all education funding, and Title I in particular, have focused on ensuring that funds flow to students and districts with the highest need. Given the persistence of achievement and opportunity gaps across race, class, and socioeconomic status, there is still work to be done on this front. Further, rapidly developing technologies such as artificial intelligence and immersive technologies are opening up new possibilities for students and teachers. However, these solutions are not enough. Realizing the full potential of funding streams and emerging technologies to transform student outcomes requires new solutions designed alongside the communities they are intended to serve. 

To finally close the equity gap, districts must invest in developing, evaluating, and implementing new solutions to meet the needs of students and families today and in a rapidly changing future. Using Title I funding to create a continuous, improvement-oriented research and development (R&D) infrastructure supporting innovations at scale will generate the systemic changes needed to reach the students in highest need of new, creative, and more effective solutions to support their learning. 

Challenge and Opportunity

Billions of dollars of federal funding have been distributed to school districts since the authorization of Title I federal funding under the Elementary and Secondary Education Act (ESEA), introduced in 1965 (later reauthorized under the Every Student Succeeds Act [ESSA]). In 2023 alone, Congress approved $18.4 billion in Title I funding. This funding is designed to provide targeted resources to school districts to ensure that students from low-income families can meet rigorous academic standards and have access to post-secondary opportunities. ESEA was authorized during the height of the Civil Rights Movement with the intent of addressing the two primary goals of (1) ensuring traditionally disadvantaged students were better served in an effort to create more equitable public education, and (2) addressing the funding disparities created by differences in local property taxes, the predominant source of education funding in most districts. These dual purposes were ultimately aimed at ensuring that a student’s zip code did not define their destiny.

The passing of ESEA was a watershed moment. Prior to its authorization, education policy was left mostly up to states and localities. In authorizing ESEA, the federal government launched ongoing involvement in public education and initiated a focus on principles of equity in education.

Further, research shows that school spending matters: Increased funding has been found to be associated with higher levels of student achievement. However, despite the increased spending for students from low-income families via Title I, the literature on outcomes of Title 1 funding is mixed. The limited impact of Title I funds on outcomes may be a result of municipalities using Title I funding to supplant or fill gaps in their overall funding and programs, instead of being used as an additive funding stream meant to equalize funding between poorer and richer districts. Additionally, while a taxonomy of options is provided to bring rigor and research to how districts use Title funding, the narrow set of options has not yielded the intended outcomes at scale. For instance, studies have repeatedly shown that school turnaround efforts have proven particularly stubborn and not shown the hoped-for outcomes.

The equity gap that ESEA was created to address has not been erased. There is still a persistent achievement gap between high- and low-income students in the nation. The emergence of COVID in 2020 uprooted the public education system, and its impact on student learning, as measured by test scores, is profound. Students lost ground across all focus areas and grades. Now, in the post-pandemic era, students have continued to lose ground. The “COVID Generation” of students are behind where they should be, and many are disengaged or questioning the value of their public education. Chronic absenteeism is increasing across all grades, races, and incomes. These challenges create an imperative for schools and districts to deepen their understanding of the interests and needs of students and families. The quick technological advancements in the education market are changing what is possible and available to students, while also raising important questions around ethics, student agency, and equitable access to technology. It is a moment of immense potential in public education. 

Title I funds are a key mechanism to addressing the array of challenges in education ranging from equity to fast-paced advancements in technology transforming the field. In its current form, Title I allocation occurs via four distribution criteria. The majority of funding is allocated via basic grants that are determined entirely on individual student income eligibility. The other three criteria allocate funding based on the concentration of student financial need within a district. Those looking to rethink allocation often argue for considering impact per dollar allocated, beyond solely need as a qualifying indicator for funding, essentially taking into account cost of living and services in an area to understand how far additional funding will stretch in order to more accurately equalize funding. It is essential that Title I is redesigned beyond redoing the distribution formula. The money allocated must be spent differently—more creatively, innovatively, and wisely—in order to ensure that the needs of the most vulnerable students are finally met.

Plan of Action

Title I needs a new allowable spending category approved that specifically enables and encourages districts to use funds for activities that drive equity-focused innovation. Making room for innovation grounded in equity is particularly important in this present moment. Equity has always been important, but there are now tools to better understand and implement systems to address it. As school districts continue to recover from the pandemic-related disruptions, explore new edtech learning options, and prepare for an increasingly diverse population of students for the future, they must be encouraged to drive the creation of better solutions for students via adding a spending category that indicates the value the federal government sees in innovating for equity. Some of the spending options highlighted below are feasible under the current Title I language. By encouraging these options tethered specifically to innovation, district leadership will feel more flexibility to spend on programs that can foster equity-driven innovation and create space for the new solutions that are needed to improve outcomes for students.

Innovation, in this context, is any systemic change that brings new services, tools, or ways of working into school districts that improve the learning opportunities and experience for students. Equity-focused innovation refers to innovation efforts that are specifically focused on improving equity within school systems. It is a solution-finding process to meet the needs of students and families. Innovation can be new, technology-driven tools for students, teachers, or others who support student learning. But innovation is not limited to technology. Allowing Title I funding to be used for activities that support and foster equity-driven innovation could also include:

Expanding Title I funding to make room for innovative ideas and solutions within school systems has the potential to unlock new, more effective solutions that will help close equity gaps, but spending available education funds on unproven ideas can be risky. It is essential that the Department of Education issues carefully constructed guardrails to allow ample space for new solutions to emerge and scale, while also protecting students and ensuring their educational needs are still met. These guardrails and design principles would ensure that funds are spent in impactful ways that support innovation and building an evidence base. Examples of guardrails for a school system spending Title I funding on innovation could include:

While creating an authorized funding category for equity-focused innovation through Title I would have the most widespread impact, other ways to drive equitable innovation should also be pursued in the short term, such as through the new Comprehensive Center (CC), set to open in fall 2024, that will focus on equitable funding. It should prioritize developing the skills in district leaders to enable and drive equity-driven innovation. 

Conclusion

Investment in innovation through Title I funding can feel high risk compared to the more comfortable route of spending only on proven solutions. However, many ways of traditional spending are not currently working at scale. Investing in innovation creates the space to find solutions that actually work for students—especially those that are farthest from opportunity and whom Title I funding is intended to support. Despite the perceived risk, investing in innovation is not a high-risk path when coupled with a clear sense of the community need, guardrails to promote responsible R&D and piloting processes, predetermined outcome goals, and the data systems to support transparency on progress. Large-scale, federal investment in creating space for innovation through Title I funding in—an already well-known mode of district funding not currently realizing its desired impact—will create solutions within public education that give students the opportunities they need and deserve.

This action-ready policy memo is part of Day One 2025 — our effort to bring forward bold policy ideas, grounded in science and evidence, that can tackle the country’s biggest challenges and bring us closer to the prosperous, equitable and safe future that we all hope for whoever takes office in 2025 and beyond.

This memo was developed in partnership with the Alliance for Learning Innovation, a coalition dedicated to advocating for building a better research and development infrastructure in education for the benefit of all students. Read more education R&D memos developed in partnership with ALI here.

New Coalition Launches for Increased Investment in Education R&D

WASHINGTON, D.C. – On Monday the Federation of American Scientists (FAS) launched the Alliance for Learning Innovation (ALI), a bipartisan initiative co-led with Lewis-Burke Associates, LLC, to increase education research and development investments across the federal government. 

The alliance brings together a group of education nonprofits, practitioners, philanthropy, and the private sector to advocate for research-based innovations in education. As a coalition, ALI focuses on innovative solutions that build education R&D infrastructure, center students and practitioners, advance equitable outcomes for students, improve talent pathways, and expand the workforce needed in a globally competitive world. To that end, the alliance has developed a comprehensive multi-part agenda including the goal of dramatically increasing the federal investment in education R&D.

“It’s an ambitious goal, but it’s exactly what we need right now,” said FAS CEO, Dan Correa, at the launch event earlier this week at the American Enterprise Institute in Washington, DC. Michael L. Ledford, J.D., President of Lewis-Burke Associates LLC added “this is an important moment and I know the ALI coalition and many organizations in this room feel an incredible sense of urgency to act and continue to make progress.” 

Recent National Assessment of Educational Progress (NAEP) results suggest the urgent need for transformative new approaches to K-12 education and that requires greater investment in education R&D. The U.S. is experiencing the largest drop in reading scores since 1990 and the first-ever decline in math scores. This decrease is partly the result of the COVID-19 pandemic, but also of a system that was already not working for many students.

“The world is changing quickly. We need better tools to support student outcomes and we need to update the toolkit we use to support R&D in education,” said Dr. Mark Schneider, Director of the Institute of Education Sciences (IES) at the U.S. Department of Education. IES has recently been charged by Congress with using a portion of its fiscal year 2023 budget to support a new funding opportunity for quick turnaround, high-reward scalable solutions intended to significantly improve outcomes for students. Dr. Schneider is fueled with a sense of urgency to ensure this initial investment improves outcomes and builds a firm foundation for the future of a larger, more innovative federal R&D infrastructure in education.  

Dr. James Moore III, Assistant Director of the STEM Education Directorate (EDU) at the National Science Foundation built on what Dr. Schneider shared and reinforced that “we have to double-down on catalyzing opportunities throughout America, especially in places that have been traditionally under-resourced. Right now is an opportunity to think differently, to innovate on the current models, and figure out how to address the comprehensive needs of students at every juncture of education and beyond.”

Dr. Penny Schwinn, Commissioner of the Tennessee Department of Education agreed and discussed what this has looked like in Tennessee. “Without evidence-based solutions driven by R&D, we won’t have strong outcomes for kids. We are utilizing education R&D with the goal of improving student outcomes, supporting educators, and building a better education system for all learners.” 

Denise Forte, President and CEO of the Education Trust added that, “getting education R&D right requires reaching into communities and working directly with students and parents. Better applying and scaling evidence-based approaches is essential to improving education equity.” 

“We need BOTH mindset and skill set shifts to make the changes we seek,” said Josh Edelman of Transcend Education on Monday. “The current system of schooling is out of date and we need to move to 21st century learning that is learner centered.”  Kimberly Smith, Digital Promise, added that “engaging students, families and educators is critically important if the R&D work is to be equitable and effective for all students.”  

“Gen Z is optimistic about what’s possible – from our society and from our schools,” said Romy Drucker, Education Program Director, Walton Family Foundation. “ALI will help realize the ambitious vision that youth have for education, reinventing learning to be more relevant and inspiring.”

For media inquiries, please contact press@fas.org

Establishing Village Corps: A National Early Childhood Education (ECE) Program at AmeriCorps

Summary

While becoming a parent can bring great joy, having children can also impose an economic burden on families, reduce familial productivity in society, or cause one or more adults in a family — often mothers — to step back from their careers. In addition, many parents lack access to reliable information and resources related to childhood wellness, nutrition, and development.

As the saying goes, “It takes a village to raise a child.” But what if the metaphorical “village” was our entire nation? The momentum of the American Rescue Plan, as well as the spotlight that the COVID-19 pandemic focused on the demands of caretaking, provides the federal government an opportunity to create a new branch of its existing service corps — AmeriCorps — focused on early childhood education (ECE). This new “Village Corps” branch would train AmeriCorps members in ECE and deploy them to ECE centers across the country, thereby helping fill gaps in childcare availability and quality for working families. The main goals of Village Corps would be to:

Challenge and Opportunity

The COVID-19 pandemic has highlighted the vast disparity in childcare services available for families in the United States. Our nation spends only 0.3% of GDP on childcare, lagging most other countries in the Organization for Economic Cooperation and Development (OECD). Put another way, average public spending on childcare for toddlers in the United States is about $500, while the OECD average is more than $14,000 (Figure 1). The problem is compounded by the lack of mandated paid family or medical leave in most states.

Figure 1. Public spending by OECD nations on childcare. Source: The New York Times

The Child Care and Development Block Grant (CCBG)’s Child Care and Development Fund (CCDF) is the primary source of federal funding for childcare. CCDF support is intended to assist eligible families by providing subsidy vouchers for childcare. However, only one out of every nine eligible children actually receives this support, and many families who need support do not meet eligibility requirements. Furthermore, according to the National Center for Children in Poverty, the federal Early Head Start program (which includes infants and toddlers before pre-K age) serves only 3% of those eligible, leaving a major gap for families of children under the age of three.

Limited federal support for families that need childcare creates a vicious cycle. Unlike public school from kindergarten onwards, ECE and childcare facilities rely mostly on parent fees to stay open and operational. When not enough parents can afford to pay, ECE and childcare facilities will lack sufficient revenue to provide high-quality care. Indeed, the Center for American Progress found that “the true cost of licensed child care for an infant is 43 percent more than what providers can be reimbursed through the [CCDF] child care subsidy program and 42 percent more than the price programs currently charge families.” This revenue gap has resulted in a worrying hollowing of our nation’s ECE infrastructure. 51% of Americans live in an area that has few or no licensed1 childcare options. Only in high-income communities does the predominant model of parent-funded childcare provide enough high-quality ECE to meet the demand. 

Underfunding has left ECE workers barely making a living wage with little to no benefits; although there has been a heavy public focus on low K–12 teacher salaries, the situation for ECE workers is worse. The average annual salary for childcare workers falls in the lowest second percentile of occupations in the United States, versus the 61st percentile for kindergarten teachers (Figure 2). Poor working conditions and compensation create high turnover in ECE, making it even harder for ECE facilities to meet demand. 

Moreover, scholarship and policy initiatives designed to strengthen the training and satisfaction of the ECE workforce tend to focus on lead teachers. Such initiatives largely overlook the needs of assistant teachers/teacher’s aides, even though (i) these support personnel contribute meaningfully to classroom quality, and (ii) professional development at the aide level has been found to increase retention (Figure 3) and improve longer-term career outcomes. 

Figure 2. Selected occupations ranked by annual pay, 2019. Source: Center for the Study of Child Care Employment, UC Berkeley
Figure 3. Six key contributions to teacher exodus. Source: Research Brief: Solving the Teacher Shortage How to Attract and Retain Excellent Educators

These challenges merit federal intervention. Even though ECE is largely a private endeavor, high-quality and widely available early childcare and education contributes to the public good. Research shows that public investment in childcare pays for itself several times over by making it easier for parents to participate in the labor force. Additionally, spending $1 on early care and education programs has been shown to generate $8.60 in economic activity.

But it is not only the cost of childcare that is inhibitory. In 2016, two million parents made career sacrifices due to problems encountered with obtaining childcare. Mothers and single parents are especially likely to be adversely impacted by limited access to childcare. In 2020, mothers of older children remained more likely to participate in the labor force than mothers with younger children. Families are finding it increasingly difficult within the current system to find and gain access to quality childcare, leading to employment issues and an attrition of women from the workforce. Deploying a federally funded corps to fill the ECE personnel gap would stabilize ECE and childcare centers, creating a strong foundation for families and communities that will yield increased economic growth and equity. Americans have never fully benefited from a federally funded and run childcare system. It is time for the federal government and Congress to treat childcare as a public responsibility rather than a personal one

Plan of Action

Building on momentum for familial support established by the American Rescue Plan, the federal government should launch Village Corps, a new ECE-focused branch of AmeriCorps. AmeriCorps is “one of the only federal agencies tasked with elevating service and volunteerism in America.” AmeriCorps also has a long history of implementing programs in classrooms throughout the United States to “support students’ social, emotional, and academic development”, but has never had a program dedicated exclusively to training and placing Corps members in ECE. Village Corps would do just that. Participants in Village Corps would receive federally administered and/or sponsored training in fundamental aspects of high-quality ECE, including but not limited to CPR and first aid, child-abuse prevention, appropriate child and language development, classroom management, and child psychology. Village Corps members would then be placed in ECE centers across the country, providing an affordable, reliable source of infant and early childhood care for working families in the United States. Village Corps members would also have access to ongoing professional-development opportunities, enabling them to ultimately receive a Child Development Associate® (CDA) or similar tangible credential, and preparing them to pursue longer-term career opportunities in ECE.

Village Corps can be developed and deployed via the following steps:

Step 1. Establish Village Corps as a new programmatic branch of AmeriCorps.

AmeriCorps already comprises several distinct branches, including State and National, VISTA, and RSVP. Village Corps would be a new programmatic branch focused on training corps members in ECE and placing them in ECE centers nationwide. The program could start by placing corps members in Early Head Start and Head Start locations, since these are directly funded by the federal government. Piloting the program for a year at 10 sites, with five corps members per site, would require about $2 million: $1.25 million to cover salary costs, plus an additional $750,000 to subsidize living and healthcare expenses, provide an optional education credit, and account for administrative costs.

Program reach could ultimately be expanded to additional childcare centers. The federal government could even consider creating and operating a new network of ECE centers staffed predominantly or exclusively by corps members. As Village Corps develops and grows, it should prioritize placements in states, regions, and cities where a disproportionate share of the population lives in a childcare desert.

Step 2. Develop the core components of the Village Corps volunteer experience.

Recruitment and placement of Village Corps participants should follow the same general mechanisms used for other AmeriCorps divisions; however, the program should strive to place Village Corps participants in positions within their own communities. Village Corps service should be for a minimum of one year, with the option to extend to two. In addition to a modest salary, access to healthcare benefits, and a possible living stipend, Village Corps participants should receive the following benefits:

Step 3. Build a path for program funding and growth.

To start, the Biden-Harris Administration should work with the House Committee on Education and Labor and the Senate HELP Committee to see if Village Corps can be integrated into legislation like the Universal Child Care and Early Learning Act. The Administration could also consider launching Village Corps as part of the American Families Plan, and/or capitalizing on the budget reconciliation package for Build Back Better. This package is awarding $9.5 billion in grants to Head Start agencies in states that have not received payments under universal preschool programs and $2.5 billion annually for FY2022–2027 to improve compensation for Head Start staff. An additional way to make the program even more attractive would be to propose cost-matching of federal funds for Village Corps by states (if program participants are deployed in state-aided childcare centers), and/or through partnerships with key stakeholders and philanthropic organizations (e.g., Child Care Aware of America, the Child Care Network, the National Association for the Education of Young Children (NAEYC), and the First Five Year Fund) that have a history of supporting expansion and access to ECE. Given the downstream effects of ECE disparity in the workforce, capitalizing on the Defense Production Act could also be an avenue of support for Village Corps (see FAQ). For the longer term, the federal government could consider complementing Village Corps with a Federal Childcare and Education Savings Account (CESA) that would further subsidize childcare for families nationwide.

Conclusion

The COVID-19 pandemic has highlighted gaping holes in our national early childhood care and education (ECE) fabric and has significantly exacerbated a failing system. The effects of this failure are widespread, compromising familial stability and economic security, the health, and future outcomes of American children, ECE worker retention, national productivity, and workforce participation. Establishing a new ECE-focused branch of AmeriCorps is an innovative solution to a pressing issue: a solution that builds on existing programmatic infrastructure to use talent and funds efficiently and equitably. Village Corps would create a talent pipeline for future ECE educators, boost the American workforce, and make high-quality infant and childcare easily accessible to all working families. 

Frequently Asked Questions
Why should the federal government establish a new branch of AmeriCorps instead of just expanding childcare subsidies?

Current federal assistance for ECE is provided in the forms of subsidies and grants. This avenue is limited in its impact, reaching only 1 in 9 eligible families. Moreover, licensed childcare in many instances costs 43% more than what providers are eligible to be reimbursed for through federal childcare subsidies, and 42% more than what providers can sustainably charge families. This disparity between subsidized and actual costs has created a system that underpays ECE providers, resulting in lower-quality childcare and scarce availability of childcare slots for subsidy-eligible families. Additionally, because even federally subsidized ECE centers rely heavily on fees collected by families, they are at higher risk of closure during difficult times (such as the COVID-19 pandemic) than educational facilities (e.g., K–12 schools) that are fully federally funded.


The federal government could try to remedy these issues through a massive infusion of cash into childcare subsidy programs. But a national-service-oriented approach — i.e., working through AmeriCorps to direct additional human capital to ECE — is a creative and potentially more cost-efficient strategy that is worth trying.

How will centers be identified/selected for Village Corps placements?

The first suite of Village Corps participants will be placed at existing Early Head Start Centers, which must adhere to a strict set of performance standards. In later years, Village Corps could partner with state agencies or NGOs and philanthropic organizations that support ECE centers in areas characterized by childcare deserts.

Will public funding for ECE guarantee higher salaries for ECE workers?

Not directly, but it has been shown that teachers and caregivers who work in publicly funded settings earn higher wages than those in non-publicly funded settings. Hence it is reasonable to expect that public funding for ECE will translate into higher salaries for ECE workers.

How will Village Corps be incorporated into AmeriCorps and be screened/selected?

AmeriCorps currently has seven sub-programs through which it disseminates volunteers; Village Corps would become the eighth. As a sub-program of AmeriCorps, Village Corps participants would have to undergo the general AmeriCorps application process to be selected to serve. In addition, Village Corps should look for the following traits in its applicants:



  • Coachable

  • Accountable

  • Problem solver and critical thinker

  • Takes initiative and possess leadership qualities

  • Resilient

  • Adaptive

  • Excels in a fast paced/challenging environment

  • Team player

5. What is an alternative support mechanism for Village Corps?

A lack of quality ECE options has a dramatic effect on workforce participation. The market failure of undersupplied ECE options decreases economic productivity. Village Corps would address some of these market failures by stabilizing the ECE workforce and fulfilling the labor requirements for high-quality ECE centers, thereby enabling families to increase workforce participation and economic productivity. Increased workforce participation is especially important for helping the United States remain globally competitive in science, technology, engineering, and math (STEM) fields. 40% of women and 23% of men in full-time STEM jobs leave or switch to part-time work after their first child. Taken together, these facts make a compelling case for using the Defense Production Act to support Village Corps.


There is precedent for the government utilizing funds in this manner. During World War II, large-scale entry of women into the workforce created sudden and pressing demands for childcare. Congress responded by passing the Defense Housing and Community Facilities and Services Act of 1940, also known as the Lanham Act. The law funded public works — including childcare facilities — in communities that had defense industries. About 3,000 federally subsidized and run Lanham centers ultimately provided childcare for up to six days a week and certain holidays. Parents only paid the equivalent today of $10/day for care.

Improving Data Infrastructure to Meet Student and Learner Information Needs

Summary

The Congress should dedicate $1 billion, 1 percent of the proposed workforce funding under the American Jobs Plan, for needed upgrades to Statewide Longitudinal Data Systems (SLDS). Major upgrades are needed to Statewide Longitudinal Data Systems to enable states to effectively monitor and address long-term pandemic learning loss, while ensuring this generation of students stays on track for college and career in the aftermath of the pandemic. With the major influx of planned resources into K12 and postsecondary education from the recent and upcoming relief bills, there is also a critical need to ensure those funds are targeted toward students and workers who are most in need and to measure the impact of those funds on pandemic recovery. Some states, such as Texas and Rhode Island, are already leveraging funds from previous relief bills (e.g., Governor’s Emergency Education Relief Fund from the Coronavirus Aid, Relief, and Economic Security, or CARES Act), to modernize their data systems, offering a model for other states to connect education, workforce, and social services information. This demonstrates an interest and need among states for SLDS upgrades, though additional investment is necessary to address historically underfunded data infrastructure.

Doubling the R&D Capacity of the Department of Education

Summary

Congress is actively interested in ensuring that the United States is educating the talent needed to maintain our global economic and national security leadership. A number of proposals being considered by Congress focus on putting the National Science Foundation’s Education division on a doubling path over the next 5-7 years.

This memo recommends that the Institute of Education Sciences (IES) — the R&D agency housed within the Department of Education — be put on the similar doubling path with stepladder increases in authorization levels, and targeted program starts (e.g., an “ARPA” housed at ED) focused on major gaps that have been building for years but made even more evident during the pandemic.

This increased funding for IES should be focused on:

• Establishing New Research Capacity in the form of an [1] “ARPA-like” Transformative Research Program;

• Harnessing Data for Impact through investments in [2] Statewide Longitudinal Data Systems (SLDS), [3] a Learning Observatory, and [4] modernization of the National Assessment of Education Progress (NAEP);

• Conducting Pathbreaking Data-Driven Research by [5] building a permanent Data Science Unit within IES, [6] increasing funding for special education research; and [7] investing in digital learning platforms as research infrastructure; and

• Building the Education Field for Deployment of What Works by [8] establishing a Center of Learning Excellence for state-level recovery investments in tutoring and more.

Investing in Community Learning Ecosystems

Summary

Developed during a different industrial era, today’s education system was never designed to meet modern learners’ needs. This incongruity has heaped systemic problems upon individual educators, blunted the effectiveness of reforms, and shortchanged the nation’s most vulnerable young people — outcomes exposed and exacerbated by COVID-19. Building back better in a post-pandemic United States will require federal investments not only in schools, but in “learning ecosystems” that leverage and connect the assets of entire communities. Tasked with studying, seeding, and scaling these ecosystems in communities across the country, a White House Initiative on Community Learning Ecosystems would signal a shift toward a new education model, positioning the United States as a global leader in learning.

Improving Learning through Data Standards for Educational Technologies

The surge in education technology use in response to COVID-19 represents a massive natural experiment: an opportunity to learn what works at scale, for which students, and under which conditions. However, without the right data standards in place we risk incomplete or inaccurate inferences from this experiment.

The COVID-19 pandemic has dramatically increased use of educational technologies. There is evidence that this “emergency onlining” will lead to learning loss, especially among underserved communities. To understand and address the extent of learning loss—as well as to explore and support potential future uses of educational technologies—the U.S. Department of Education (ED) must systematically implement established open-data standards that allow us to understand how students engage with learning technologies. Widescale implementation of these standards will make it possible to combine and analyze validated data sets generated by multiple technologies. This in turn will provide unprecedented, on-demand reporting and research capabilities that can be used to precisely identify gaps and create targeted interventions. Specifically, we recommend that ED mandate the use of the open Experience API (xAPI) standard for educational technology purchased with federal funds. We further recommend that ED invest time, talent, and resources to further develop this standard and pilot efforts to leverage educational-technology data for insights through the Institute for Education Sciences (IES) and other agencies.

Challenge and Opportunity

The COVID-19 pandemic rapidly forced schools across the country to close physical campuses and convert all instruction to an “emergency online” modality for much of 2020. The situation will likely persist well into 2021. The emergency shift to online teaching meant that many teachers had insufficient preparation to successfully adapt classroom-teaching methods for digital formats. Moreover, many students—especially those from low-income families or from historically underserved racial and ethnic groups—lack access to high-speed broadband and technology assets needed to fully participate in online learning. These factors are combining to create learning losses that exacerbate our existing digital divides that may persist for years.

Robust educational research and development is needed to fully understand the extent and distribution of learning loss, as well as to develop interventions for addressing it. Educational technologies—which record all student interactions, from logins to mouse-clicks to assignment submissions—could provide a wealth of data on how online education is succeeding and/or falling short. Unfortunately, these data are frequently recorded in a way that is unique to each application. This lack of consistency makes it difficult to integrate educational data or make comparisons between institutions

The time is ripe to introduce new requirements for learning technology designed to ensure that parents, educators, administrators, and stakeholders at every level can assess where students are at, what they know, and what will best help them to advance. These insights could also significantly reduce the day-to-day demands on teachers’ time and attention, enabling them to focus on deeper student questions. The technology needed to implement such requirements are already available in the open-source xAPI standard, which is currently in the final stages of approval as an IEEE standard. Further, there are xAPI “profiles” that define specific data requirements for processes common to educational technologies, such as playing a video. While the concept of a learning-data standard was recommended by ED as early as 2015, adoption has been uneven in practice. This situation must change for us to immediately address COVID19 learning loss as quickly and accurately as possible.

Plan of Action

To address the challenges outlined above, we recommend including xAPI as a federal procurement requirement to encourage adoption among educational software and service providers. Widespread adoption will mean that most—if not ultimately all—providers consistently and automatically generate only the educational data that conforms to standards established by ED. Establishing consistent standards for educational data will make it easier for all parties to contribute meaningfully to key datasets, and for researchers to develop tools to track and exchange meaningful data. These outcomes together will deliver deeper understandings of how our nation’s students are doing, inform efforts to close achievement gaps, and facilitate tracking of changes over time. We also recommend investing ED time, talent, and resources into further developing the xAPI standard and participating in pilot projects that demonstrate its utility. Each of these recommendations is detailed further below.

Recommendation 1. Mandate use of the xAPI standard for ED-funded procurement.

We recommend that ED mandate use of xAPI for all educational technology purchased through ED directly as well as through federal grants. ED should also establish a process for ensuring compliance, including conducting conformance tests on educational software and services from different providers.

The IEEE is in the final stages of publishing the open-source xAPI standard. Mandating its adoption would demonstrate cutting-edge ED leadership. Widespread adoption of the standard will provide a common approach to collecting evidence about how students, parents, and teachers interact with education platforms, paving the way for much more rigorous, consistent, and reliable educational research.

Recommendation 2. Develop xAPI profiles to facilitate data integration and improve data quality for the educational sector.

IEEE is standardizing documents that help automate the data governance needs for a type of educational solution (“application profiles”). Standards developers are rarely familiar with learning sciences and educational research. As a result, xAPI profiles will tend to be general in nature unless domain-specific experts get involved. For instance, medical experts have worked to develop the MedBiquitious xAPI Profiles for Medical Education. Human-resources experts have developed the Human Resources Open Standards (HROS) xAPI Profile and, and work is ongoing for an Assessment xAPI Profile that supports the U.S. Chamber of Commerce T3 initiative.

ED should invest time, talent, and funding to develop xAPI profiles that are aligned with current research and national priorities. An xAPI Profile effort could help to normalize data collection from a spate of popular 5th grade mobile math applications, that properly identify the relevant ED standards, competencies or objectives are challenged by a student, which could provide such app developers with the automation that would simplify generating better, aligned data. Works like this could change online classrooms into opportunities to embed better pedagogy into practice at scale.

Recommendation 3. Invest in applied research and development.

ED should partner with schools and educational technology companies to invest in applied research that demonstrates insights from standardized educational data. ED should also work with partners to invest in public repositories of code to make it easier for all stakeholders to leverage insights. Such investments should focus both on the short term (e.g., providing immediate insights about use of educational technology and learning loss during the COVID-19 pandemic) and long term (e.g., providing examples of potential applications that could be scaled and replicated in the future). Such investments would not only advance our understanding of education, but would also help to develop a market for further development of data-based educational products. IES and ED’s Office of Educational Technology should partner to identify topics and approaches to conduct this cutting-edge research.

There are multiple examples of research using educational-process data that these investments could build on. IES recently issued a request for proposals (RFP) to use National Assessment of Educational Progress (NAEP) process data to identify students with disabilities, to understand how those student use available accommodations, and to determine which are most successful. Predictive models of student dropout risk, course design analytics to identify areas for improvement, and course-taking patterns are all being conducting using this data at relatively small scale through academic societies, such as the Society for Learning Analytics Research (SoLAR) and the International Educational Data Mining Society. All stand to benefit and leverage this data to dramatically improve research.

SoLAR recently published a position paper describing current challenges with these data.4 Larger educational-research societies such as the American Educational Research Association (AERA) and the National Council on Measurement in Education (NCME) have launched specialized groups focused on working with educational-process data.

By investing in this area, ED could help to nurture this area of research and make a difference in the lives of students, parents, teachers and schools across the country. This approach would help to motivate better data quality and enable technologists to build more robust learning applications, thereby helping us to stem the COVID-19 learning loss as quickly as possible using contemporary science and technology.

Securing the Nation’s Educational Technology

Summary

Never before have so many children in America used so much educational technology, and never before has it been so important to ensure that these technologies are secure. Currently, however, school administrators are overburdened with complex security considerations that make it challenging for them to keep student data secure. The educational technologies now common in America’s physical and virtual classrooms should meet security standards designed to protect its students. As a civil rights agency, the Department of Education has a responsibility to lead a coordinated approach to ensuring a baseline of security for all students in the American education system.

This policy initiative will support America’s students and schools at a time when educational experiences—and student information—are increasingly online and vulnerable to exploitation. The plan of action outlined below includes a new Department of Education educational technology security rule, training support for schools, a voluntary technology self-certification system, an online registry of certified technologies to help grow a secure educational technology market, and processes for industry support and collaboration in this work. Combined, these efforts will create a safer digital learning environment for the nation’s students and a more robust educational technology marketplace.

Using Online Tutoring to Address COVID-19 Learning Loss and Create Jobs

Summary

The Biden-Harris Administration should create a plan for a public, online platform to connect teachers with college students and recent graduates to serve as tutors for K-12 students. One-on-one tutoring is a proven intervention that improves children’s educational competencies and increases students’ self-confidence. Along with supporting students, this platform could provide needed employment for young adults and enable teachers and students together to produce improved educational outcomes. The COVID-19 pandemic has led to the closure of more than 124,000 schools with the majority of students now learning online. Meanwhile, millions of college students have lost part-time work or are graduating into a historically difficult job market that does not have positions for them to fill. Just as the New Deal created work programs that both created employment and improved our national landscape, our country requires creative solutions that can meet the urgent needs of our time, can be quickly scaled up using modern technology and can adjust to the changing needs dictated by the cycles of the coronavirus.

A Focus on Teacher Effectiveness, Shortages, and Cultural Proficiency

Summary

Addressing inequality, closing achievement gaps, and tackling opportunity gaps in schools requires a highly effective educator in every classroom, a diversified teacher workforce, and an implementation of culturally responsive policies and practices. The 2015 Every Student Succeeds Act (ESSA) requires State Education Agencies (SEA) to identify and close gaps in equitable access to effective teachers but does not offer specific definitions about what constitutes teacher effectiveness. There is an opportunity to build on state equity plans and collaboratively work with districts, schools, educator preparation programs, and other stakeholders to close the gap in access to effective educators, diversify the workforce, and ensure that the training of educators includes a focus on culturally proficient practices.

Ending Violence in Schools

Summary

Tens of thousands of students experience violence in schools in the form of corporal punishment. Nineteen states continue to allow for corporal punishment as a means of disciplining students in public schools. And public schools in nine states use corporal punishment as a disciplinary strategy for preschool-aged children. There is no federal law or regulation governing the practice, however the federal government should be clear that it does not condone it.