View in EDS

Empowering Non-Verbal Individuals through AI-Driven Symbolic Text Prediction: A Metaliteracy Approach to Communication and Inclusion

Saved in:
Bibliographic Details
Title: Empowering Non-Verbal Individuals through AI-Driven Symbolic Text Prediction: A Metaliteracy Approach to Communication and Inclusion
Language: English
Authors: Melissa Beck Wells
Source: Discover Education. 2025 4.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 12
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Augmentative and Alternative Communication, Prediction, Interpersonal Communication, Access to Education, Higher Education, Privacy, Barriers, Ethics
DOI: 10.1007/s44217-025-00809-8
ISSN: 2731-5525
Abstract: The integration of artificial intelligence (AI) into augmentative and alternative communication (AAC) systems has revolutionized the way non-verbal individuals interact with their environment. AI-powered symbolic text prediction offers innovative solutions to enhance expressive and receptive communication, promoting autonomy and social inclusion. This article examines the role of AI-driven predictive text within the frameworks of metaliteracy and Universal Design for Learning (UDL), emphasizing its potential to create adaptive and ethical digital communication environments. Recent advancements in machine learning models and natural language processing (NLP) have contributed to more sophisticated AAC tools; however, challenges such as bias in predictive algorithms, accessibility limitations, and ethical considerations persist. This study critically evaluates the benefits, challenges, and future directions of AI-powered symbolic text prediction, particularly in educational, therapeutic, and social settings. The findings highlight the need for equitable AI design that accounts for diverse linguistic and cognitive needs, reinforcing AI's role in fostering digital inclusivity and empowering non-verbal individuals to become autonomous communicators.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1489143
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1489143
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Empowering Non-Verbal Individuals through AI-Driven Symbolic Text Prediction: A Metaliteracy Approach to Communication and Inclusion
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Melissa+Beck+Wells%22">Melissa Beck Wells</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Discover+Education%22"><i>Discover Education</i></searchLink>. 2025 4.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 12
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Augmentative+and+Alternative+Communication%22">Augmentative and Alternative Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Interpersonal+Communication%22">Interpersonal Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Access+to+Education%22">Access to Education</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22Privacy%22">Privacy</searchLink><br /><searchLink fieldCode="DE" term="%22Barriers%22">Barriers</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics%22">Ethics</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s44217-025-00809-8
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2731-5525
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The integration of artificial intelligence (AI) into augmentative and alternative communication (AAC) systems has revolutionized the way non-verbal individuals interact with their environment. AI-powered symbolic text prediction offers innovative solutions to enhance expressive and receptive communication, promoting autonomy and social inclusion. This article examines the role of AI-driven predictive text within the frameworks of metaliteracy and Universal Design for Learning (UDL), emphasizing its potential to create adaptive and ethical digital communication environments. Recent advancements in machine learning models and natural language processing (NLP) have contributed to more sophisticated AAC tools; however, challenges such as bias in predictive algorithms, accessibility limitations, and ethical considerations persist. This study critically evaluates the benefits, challenges, and future directions of AI-powered symbolic text prediction, particularly in educational, therapeutic, and social settings. The findings highlight the need for equitable AI design that accounts for diverse linguistic and cognitive needs, reinforcing AI's role in fostering digital inclusivity and empowering non-verbal individuals to become autonomous communicators.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1489143
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1489143
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s44217-025-00809-8
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Augmentative and Alternative Communication
        Type: general
      – SubjectFull: Prediction
        Type: general
      – SubjectFull: Interpersonal Communication
        Type: general
      – SubjectFull: Access to Education
        Type: general
      – SubjectFull: Higher Education
        Type: general
      – SubjectFull: Privacy
        Type: general
      – SubjectFull: Barriers
        Type: general
      – SubjectFull: Ethics
        Type: general
    Titles:
      – TitleFull: Empowering Non-Verbal Individuals through AI-Driven Symbolic Text Prediction: A Metaliteracy Approach to Communication and Inclusion
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Melissa Beck Wells
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-electronic
              Value: 2731-5525
          Numbering:
            – Type: volume
              Value: 4
          Titles:
            – TitleFull: Discover Education
              Type: main
ResultId 1