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Related Concept Videos

Language Development01:22

Language Development

799
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
799
Learning Disabilities01:25

Learning Disabilities

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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
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Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism
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Enhancing Developmental Language Disorder Identification with Artificial Intelligence: Development of an Explainable

Georgios P Georgiou1,2

  • 1Department of Languages and Literature, University of Nicosia, Nicosia, Cyprus. georgiou.georg@unic.ac.cy.

Journal of Autism and Developmental Disorders
|December 13, 2025
PubMed
Summary
This summary is machine-generated.

This study identifies key language markers to detect developmental language disorder (DLD) in children. An AI-powered app was developed and validated, showing high accuracy in identifying DLD, aiding early diagnosis.

Keywords:
Developmental language disorderIdentificationMachine learningScreening app

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Area of Science:

  • Linguistics
  • Developmental Psychology
  • Artificial Intelligence

Background:

  • Developmental Language Disorder (DLD) affects language acquisition in children.
  • Accurate and timely diagnosis of DLD is crucial for intervention.
  • Existing diagnostic methods can be time-consuming and subjective.

Purpose of the Study:

  • To identify linguistic markers differentiating children with DLD from typically developing (TD) peers.
  • To develop an explainable Artificial Intelligence (AI)-based screening application for DLD.
  • To assess the reliability and feasibility of the AI screening app.

Main Methods:

  • A cohort of 30 children (15 DLD, 15 TD, aged 7-10) underwent verbal assessments.
  • Linguistic measures included vocabulary, morphosyntax, and sentence repetition.
  • A random forest classifier was trained on synthetic data to create the AI screening app.

Main Results:

  • Significant linguistic differences were found between DLD and TD groups across all measures.
  • The AI screening app demonstrated high concordance with expert clinical diagnoses.
  • The app proved reliable in identifying children with DLD.

Conclusions:

  • Specific linguistic indicators hold diagnostic value for DLD.
  • An AI-driven screening solution is feasible and offers practical advantages.
  • The app's interpretability and scalability can improve DLD detection, especially in under-resourced areas.