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A National Context of Generative AI for Reading to Support English Learners

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Title: A National Context of Generative AI for Reading to Support English Learners
Language: English
Authors: Stefani Pautz Stephenson, Tiffany Leones, Danae Kamdar, Yenda Prado, Joshua Ddamulira, Digital Promise
Source: Digital Promise. 2025.
Availability: Digital Promise. 1001 Connecticut Avenue NW Suite 935, Washington DC 20036. Tel: 202-450-3675; e-mail: contact@digitalpromise.org; Web site: https://digitalpromise.org/
Peer Reviewed: N
Page Count: 37
Publication Date: 2025
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305C240040
Document Type: Reports - Research
Education Level: Elementary Education
Descriptors: Artificial Intelligence, Technology Uses in Education, Reading Instruction, English Learners, Equal Education, Elementary School Students, Social Emotional Learning, Tutoring, Student Evaluation, Individualized Instruction, Barriers, Social Bias, Racism, Culturally Relevant Education, Stereotypes, Privacy
Abstract: Supported by an Institute of Education Sciences grant, the U-GAIN Reading R&D Center is investigating artificial intelligence (AI) applications aligned with reading science to achieve equitable gains for diverse elementary students, including English Learners (ELs). This report explores how generative AI (GenAI) can be used by schools to enhance reading instruction, particularly for ELs, in light of declining national literacy rates. The study outlined in this report involved a market scan of edtech products, interviews, and listening sessions with 16 educators across 11 states. Findings indicate that while many districts prioritize the Science of Reading, implementation varies. The market scan revealed that platforms are increasingly incorporating elements that reflect the whole child based on the Science of Reading. This includes the incorporation of social-emotional learning elements into stories, engaging 1:1 tutoring opportunities, and real-time assessment and micro-interventions. Educators interviewed indicated that they are integrating AI tools primarily for personalized learning and language support. They also indicated frequently using generative AI tools for lesson planning. Key opportunities discussed include uses of AI to customize texts to student interests and reading levels, engage learners in adaptive listening and speaking activities, and foster natural discussion through the use of culturally responsive content, particularly for ELs. Significant concerns were also raised regarding biases and stereotypes in AI-generated content and images, inaccuracies in Automated Speech Recognition (ASR) for diverse accents, cultural biases, and misuses of student data privacy. The report concludes with recommendations for designing AI tools with ELs at the forefront within the Science of Reading context. This includes ensuring transparency in AI development, as well as establishing robust district policies for vetting AI tools to ensure ethical and culturally responsive adoption.
Abstractor: ERIC
IES Funded: Yes
Entry Date: 2026
Accession Number: ED678826
Database: ERIC
FullText Text:
  Availability: 0
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  Data: A National Context of Generative AI for Reading to Support English Learners
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Stefani+Pautz+Stephenson%22">Stefani Pautz Stephenson</searchLink><br /><searchLink fieldCode="AR" term="%22Tiffany+Leones%22">Tiffany Leones</searchLink><br /><searchLink fieldCode="AR" term="%22Danae+Kamdar%22">Danae Kamdar</searchLink><br /><searchLink fieldCode="AR" term="%22Yenda+Prado%22">Yenda Prado</searchLink><br /><searchLink fieldCode="AR" term="%22Joshua+Ddamulira%22">Joshua Ddamulira</searchLink><br /><searchLink fieldCode="AR" term="%22Digital+Promise%22">Digital Promise</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Digital+Promise%22"><i>Digital Promise</i></searchLink>. 2025.
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  Data: Digital Promise. 1001 Connecticut Avenue NW Suite 935, Washington DC 20036. Tel: 202-450-3675; e-mail: contact@digitalpromise.org; Web site: https://digitalpromise.org/
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  Data: 37
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  Data: 2025
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Supported by an Institute of Education Sciences grant, the U-GAIN Reading R&D Center is investigating artificial intelligence (AI) applications aligned with reading science to achieve equitable gains for diverse elementary students, including English Learners (ELs). This report explores how generative AI (GenAI) can be used by schools to enhance reading instruction, particularly for ELs, in light of declining national literacy rates. The study outlined in this report involved a market scan of edtech products, interviews, and listening sessions with 16 educators across 11 states. Findings indicate that while many districts prioritize the Science of Reading, implementation varies. The market scan revealed that platforms are increasingly incorporating elements that reflect the whole child based on the Science of Reading. This includes the incorporation of social-emotional learning elements into stories, engaging 1:1 tutoring opportunities, and real-time assessment and micro-interventions. Educators interviewed indicated that they are integrating AI tools primarily for personalized learning and language support. They also indicated frequently using generative AI tools for lesson planning. Key opportunities discussed include uses of AI to customize texts to student interests and reading levels, engage learners in adaptive listening and speaking activities, and foster natural discussion through the use of culturally responsive content, particularly for ELs. Significant concerns were also raised regarding biases and stereotypes in AI-generated content and images, inaccuracies in Automated Speech Recognition (ASR) for diverse accents, cultural biases, and misuses of student data privacy. The report concludes with recommendations for designing AI tools with ELs at the forefront within the Science of Reading context. This includes ensuring transparency in AI development, as well as establishing robust district policies for vetting AI tools to ensure ethical and culturally responsive adoption.
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  Data: 2026
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  Data: ED678826
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 37
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Reading Instruction
        Type: general
      – SubjectFull: English Learners
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      – SubjectFull: Equal Education
        Type: general
      – SubjectFull: Elementary School Students
        Type: general
      – SubjectFull: Social Emotional Learning
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      – SubjectFull: Tutoring
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      – SubjectFull: Student Evaluation
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      – SubjectFull: Individualized Instruction
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      – SubjectFull: Barriers
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      – SubjectFull: Stereotypes
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      – SubjectFull: Privacy
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      – TitleFull: A National Context of Generative AI for Reading to Support English Learners
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              Y: 2025
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