Inside IES Research

Notes from NCER & NCSER

AI-Augmented Learning for Individuals with Disabilities: New Funding Opportunity, Current Research, and the Potential for Improving Student Outcomes

This March, IES Director Mark Schneider released a blog in which he discussed exploring a partnership with the National Science Foundation (NSF) to encourage scientists with expertise in AI and related fields to address the important post-pandemic need for accelerating learning. IES is now excited to announce our resulting participation in NSF’s National Artificial Intelligence (AI) Research Institutes—Accelerating Research, Transforming Society, and Growing the American Workforce solicitation. In this blog, we describe this new funding opportunity, provide examples of existing NCSER-funded research in this area, and highlight the potential for such research to further improve outcomes for learners with disabilities.

 Artificial Intelligence Research Funding Opportunity

With funding from the American Rescue Plan, NCSER plans to support research under Theme 6, Track B: AI-Augmented Learning for Individuals with Disabilities. Proposals must discuss how the work will respond to the needs of learners with or at risk for a disability in an area where the COVID-19 pandemic has further widened existing gaps and/or resulted in decreased access and opportunities for students with disabilities to learn and receive support services. Please review the solicitation, the webinar (November 16), and the frequently asked questions for more information. Interested applicants should note the primary focus of this institute:

The primary focus of an institute in AI-Augmented Learning includes research and development of AI-driven innovations to radically improve human learning and education. Achievement and opportunity gaps, particularly for learners from disadvantaged or underserved communities, have always been present, but COVID-19 has exacerbated them. Institute plans for this theme should address and measure outcomes with direct education impact, in both the short- and long- term, that have practical significance to educators, parents, or other decision-makers. Plans must also directly address algorithmic bias, model transparency, security and data privacy in the support of learning.”

Current NCSER-Funded Grants Applying Artificial Intelligence and Machine Learning

Prior to the new collaboration between IES and NSF, NCSER funded several grants that apply artificial intelligence and machine learning approaches, including those described below.

With a 2018 NCSER grant, Dr. Maithilee Kunda and her team at Vanderbilt University are building on a technology-based intervention known as Betty’s Brain. This computer-based instructional program for middle school science, designed with the support of a 2006 NCER grant, allows students to teach a computer agent to understand certain concepts, increasing their own knowledge and understanding. Dr. Kunda and her team are developing a new game called Film Detective, which is designed to improve theory of mind (ToM) reasoning in adolescents with autism spectrum disorder (ASD). More information about this project can be found in this IES blog.

With a 2021 NCSER grant, Dr. Patrick Kennedy and his team at University of Oregon are using machine learning to validate a well-known assessment, Dynamic Indicators of Basic Early Literacy Skills, 8th Edition® (DIBELS 8) as a screener for dyslexia. As of 2020, 47 states require that students be screened for dyslexia in early elementary school and many states use DIBELS for this screening. However, it remains to be validated for this purpose. To address the validity of the DIBELS for screening, this research team is using machine learning approaches to predict and classify scores in relation to a pre-defined target. This will allow the research team to draw conclusions about the validity of the DIBELS 8 for dyslexia screening. These conclusions will be disseminated widely to state and local education agencies and other stakeholders.

The Potential of AI for Improving Outcomes for Learners with Disabilities

In addition to the work that IES is funding, AI has already demonstrated potential for improving outcomes for learners with disabilities in many other ways:

  • AI has been used to support children with ASD who have difficulties understanding people’s emotions, with AI-driven apps and robots helping students practice emotion recognition and other social skills.
  • AI has informed the development of algorithms that can help those involved in assessment identify disabilities in students, such as ASD, specific learning disabilities (dyslexia, dysgraphia, and dyscalculia), and attention-deficit/hyperactivity disorder (ADHD).
  • AI-embedded interventions have included error analysis to inform instruction and personalized feedback in spelling and math for students with disabilities.

Despite these advancements, there appear to be persistent gaps in AI research for students with disabilities, such as AI for students with intellectual and developmental disabilities. This is an especially important area of work because many of these learners have multiple disabilities and/or serious health conditions. For example, children with intellectual and developmental disabilities who also have hearing loss or visual impairment have compounded challenges. Some students with Down syndrome also have hearing loss and other health complications, such as cardiac issues. AI affords an opportunity to integrate health information across different applications to improve the quality of life for these students. These technological solutions can assist in managing information about the students and communicating health information between teachers, physicians, and caregivers.

AI has the potential to transform special education. We hope that this NCSER-NSF partnership will encourage researchers to be creative in planning projects that move the field of AI forward as well as provide innovative solutions to support learners with disabilities.

This blog was co-authored by Sarah Brasiel (Sarah.Brasiel@ed.gov), program officer at NCSER and Bennett Lunn (Bennett.lunn@ed.gov), Truman-Albright Fellow for NCSER and the National Center for Education Research (NCER). IES encourages special education researchers to partners with experts in Artificial Intelligence to submit to this NSF AI Institute solicitation 22-502 to increase the evidence base on use of AI for this population.

Information and Advising: Identifying Effective Strategies that Help Students Navigate Postsecondary Education

The college pipeline from start to finish is an extraordinarily complex process with numerous decision points, options, and obstacles. Students from advantaged social backgrounds are more likely than those from disadvantaged backgrounds to attend schools and colleges staffed with advisers and support staff that have time and resources to assist them. They may also draw on relationships with family, adults in their communities, or knowledgeable peers for assistance in navigation decisions. For students without such supports, the sequence of choices may become so overwhelming that they respond by delaying decisions or making poor choices that lead to sizable delays in their degree progression. With these challenges in mind, IES has funded three information and advising initiatives that draw on insights from researchers, practitioners, and the research literature.

Technical Working Group Meeting

In July 2019, NCER convened a technical working group of 14 researchers and practitioners for a set of conversations structured around three intervention strategies that have garnered substantial attention over the last 5 years: nudges and other light-touch informational campaigns; intensive, proactive coaching and advising; and comprehensive approaches that comprise advising and other supports such as technology and financial incentives. Researchers and practitioners shared perceptions about the effectiveness of each strategy, its relevance to targeted student populations, and conditions for implementation. At the end of the day, working group members provided recommendations (see the Technical Working Group Meeting Summary for a full list), including the following:  

  • Institutions should help determine what strategies get tested, apply for research grants, and participate in the research as it progresses.
  • Research is needed that addresses the large amount of information that students face and that identifies the types of information that students respond to and act on.
  • Replication studies should be designed to measure the effectiveness of promising intervention strategies for specific student groups, with the goal of enhancing effectiveness and cost-effectiveness.

Practice Guide on Effective Advising for Postsecondary Students

In October 2021, the What Works Clearinghouse (WWC) released a Practice Guide on Effective Advising for Postsecondary Students. The practice guide includes four evidence-based recommendations designed for an audience of administrators and staff at community colleges, 4-year institutions, and other public or private technical colleges who are responsible for designing and/or delivering advising to students:

  • Intentionally design and deliver comprehensive, integrated advising that incorporates academic and non-academic supports to empower students to reach their educational goals.
  • Transform advising to focus on the development of sustained, personalized relationships with individual students throughout their college career.
  • Use mentoring and coaching to enhance comprehensive, integrated advising in ways that support students’ achievement and progression.
  • Embed positive incentives in intentionally designed advising structures to encourage student participation and continued engagement.

Gap Analysis of Information and Advising Research and Practice

In March 2020, the Lead Team of the College Completion Network began a project aimed at identifying gaps in the research evidence base for information and advising strategies. The project is organized into three parts:

  1. A systematic review of the research literature, documenting evidence of the effect of information and advising policies, practices, and programs on student outcomes
  2. A scan of information and advising policies, practices, and programs that colleges use to improve student outcomes
  3. A gap analysis to compare the findings from the scan to the findings from the systematic review to look for effective practices that are not widely implemented and promising practices in the field that have not been evaluated

The team plans to report its full set of findings by December 2021. College Completion Network study descriptions are available here: https://collegecompletionnetwork.org/studies.


This blog is the second in a blog series on Effective Postsecondary Interventions that highlights interventions with evidence of effectiveness generated through IES-funded research. For the first blog in the series, please see here.

Written by James Benson (James.Benson@ed.gov), a Program Officer for Postsecondary Education within NCER’s Policy and Systems Division, and Felicia Sanders (Felicia.Sanders@ed.gov), a Program Officer for the What Works Clearinghouse within NCEE’s Knowledge Use Division.