View in EDS PDF Full Text

Optimising Dyslexia Intervention Leveraging a Mobile Adaptive Multi-Sensory AI Model with Real-Time Speech Analytic.

Saved in:
Bibliographic Details
Title: Optimising Dyslexia Intervention Leveraging a Mobile Adaptive Multi-Sensory AI Model with Real-Time Speech Analytic.
Authors: Darni, Resmi1 resmidarni@ft.unp.ac.id, Harisman, Yulyanti1 yulyanti_h@fmipa.unp.ac.id, Putri, Lili Dasa1 lilidasaputri@fip.unp.ac.id, Fitriana, Efi2 efi.fitriana@unpad.ac.id, Sulistiobudi, Rezki Ashriyana2 rezki.ashriyana@unpad.ac.id, Herayono, Andhika1 andhikaherayono99@gmail.com
Source: International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 9, p124-138. 15p.
Subjects: Dyslexia, Phonological decoding, Instructional systems, Voice analysis, Elementary education, Perceptual learning, Artificial intelligence, People with dyslexia
Abstract: Phonological dyslexia is a neurodevelopmental learning disorder characterised by persistent difficulties in phoneme-grapheme mapping, phonological decoding, and pronunciation accuracy. Conventional dyslexia interventions are often non-adaptive, therapist-dependent, and limited in scalability, reducing their effectiveness in inclusive education contexts. This study aims to develop and evaluate a mobile adaptive multi-sensory artificial intelligence (AI) model integrated with real-time speech analytics to optimise phonological dyslexia intervention. The study employed a research and development (R&D) approach using the ADDIE framework, combined with a quasi-experimental pre- and post-test control group design. Participants were elementary school learners diagnosed with phonological dyslexia. The proposed system integrates multi-sensory phonological training, adaptive difficulty adjustment, and real-time speech analytics to provide immediate feedback and personalised learning pathways. Data were collected through expert validation, usability questionnaires, phonological reading tests, and speech analytics metrics, including phoneme error rate (PER) and word error rate (WER). [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 193831338
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Optimising Dyslexia Intervention Leveraging a Mobile Adaptive Multi-Sensory AI Model with Real-Time Speech Analytic.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Darni%2C+Resmi%22">Darni, Resmi</searchLink><relatesTo>1</relatesTo><i> resmidarni@ft.unp.ac.id</i><br /><searchLink fieldCode="AR" term="%22Harisman%2C+Yulyanti%22">Harisman, Yulyanti</searchLink><relatesTo>1</relatesTo><i> yulyanti_h@fmipa.unp.ac.id</i><br /><searchLink fieldCode="AR" term="%22Putri%2C+Lili+Dasa%22">Putri, Lili Dasa</searchLink><relatesTo>1</relatesTo><i> lilidasaputri@fip.unp.ac.id</i><br /><searchLink fieldCode="AR" term="%22Fitriana%2C+Efi%22">Fitriana, Efi</searchLink><relatesTo>2</relatesTo><i> efi.fitriana@unpad.ac.id</i><br /><searchLink fieldCode="AR" term="%22Sulistiobudi%2C+Rezki+Ashriyana%22">Sulistiobudi, Rezki Ashriyana</searchLink><relatesTo>2</relatesTo><i> rezki.ashriyana@unpad.ac.id</i><br /><searchLink fieldCode="AR" term="%22Herayono%2C+Andhika%22">Herayono, Andhika</searchLink><relatesTo>1</relatesTo><i> andhikaherayono99@gmail.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2026, Vol. 20 Issue 9, p124-138. 15p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Dyslexia%22">Dyslexia</searchLink><br /><searchLink fieldCode="DE" term="%22Phonological+decoding%22">Phonological decoding</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br /><searchLink fieldCode="DE" term="%22Voice+analysis%22">Voice analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+education%22">Elementary education</searchLink><br /><searchLink fieldCode="DE" term="%22Perceptual+learning%22">Perceptual learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22People+with+dyslexia%22">People with dyslexia</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Phonological dyslexia is a neurodevelopmental learning disorder characterised by persistent difficulties in phoneme-grapheme mapping, phonological decoding, and pronunciation accuracy. Conventional dyslexia interventions are often non-adaptive, therapist-dependent, and limited in scalability, reducing their effectiveness in inclusive education contexts. This study aims to develop and evaluate a mobile adaptive multi-sensory artificial intelligence (AI) model integrated with real-time speech analytics to optimise phonological dyslexia intervention. The study employed a research and development (R&D) approach using the ADDIE framework, combined with a quasi-experimental pre- and post-test control group design. Participants were elementary school learners diagnosed with phonological dyslexia. The proposed system integrates multi-sensory phonological training, adaptive difficulty adjustment, and real-time speech analytics to provide immediate feedback and personalised learning pathways. Data were collected through expert validation, usability questionnaires, phonological reading tests, and speech analytics metrics, including phoneme error rate (PER) and word error rate (WER). [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193831338
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3991/ijim.v20i09.61571
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 124
    Subjects:
      – SubjectFull: Dyslexia
        Type: general
      – SubjectFull: Phonological decoding
        Type: general
      – SubjectFull: Instructional systems
        Type: general
      – SubjectFull: Voice analysis
        Type: general
      – SubjectFull: Elementary education
        Type: general
      – SubjectFull: Perceptual learning
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: People with dyslexia
        Type: general
    Titles:
      – TitleFull: Optimising Dyslexia Intervention Leveraging a Mobile Adaptive Multi-Sensory AI Model with Real-Time Speech Analytic.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Darni, Resmi
      – PersonEntity:
          Name:
            NameFull: Harisman, Yulyanti
      – PersonEntity:
          Name:
            NameFull: Putri, Lili Dasa
      – PersonEntity:
          Name:
            NameFull: Fitriana, Efi
      – PersonEntity:
          Name:
            NameFull: Sulistiobudi, Rezki Ashriyana
      – PersonEntity:
          Name:
            NameFull: Herayono, Andhika
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: 2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 18657923
          Numbering:
            – Type: volume
              Value: 20
            – Type: issue
              Value: 9
          Titles:
            – TitleFull: International Journal of Interactive Mobile Technologies
              Type: main
ResultId 1