# Artificial intelligence (AI)-driven technologies for managing pediatric speech and language therapy: A scoping review

## TL;DR

This paper reviews how AI technologies can help manage speech therapy for children with speech disorders, showing their potential to improve diagnostics and engagement.

## Contribution

The paper provides a scoping review of AI-driven systems for pediatric speech therapy, highlighting their effectiveness and future research directions.

## Key findings

- AI systems using deep neural networks and acoustic features effectively detect speech sound disorders.
- Computer-based tools offer personalized therapy and real-time feedback, increasing child engagement.
- AI-assisted models help therapists monitor progress and adjust treatments more efficiently.

## Abstract

Despite the high demand for speech and language therapy (SLT) for children with speech sound disorders (SSDs), accessible services remain limited. Technology-driven efforts have led to the development of systems and applications to assist children, parents, and therapists in the SLT process. AI and machine learning (ML), particularly through automatic speech recognition and audio processing techniques, play a central role in these advancements. This scoping review examines studies focusing on these techniques for managing the SLT process.

To include the most relevant studies, a systematic search was conducted on 3 February 2025 across five major databases (PubMed, Scopus, ScienceDirect, ACM Digital Library, and IEEE Xplore), following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews guidelines. After applying our criteria, 30 of the 188 identified studies met the eligibility requirements.

These studies predominantly utilize deep neural networks, ML classifiers, acoustic features, and audio processing techniques to detect SSDs. The findings demonstrate the effectiveness of these applications to support therapists in diagnostics. Moreover, computer-based tools have proven more engaging for children than traditional therapy by offering personalized therapy plans and real-time feedback. These systems enable therapists to monitor progress and adjust treatments.

This review provides an overview of AI-assisted SLT models, highlights gaps, and suggests directions for future research. It shows the effectiveness and potential of AI in enhancing the SLT process. However, challenges related to data privacy, accessibility, and the need for clinical validation persist and need to be addressed in the future.