Novel Approaches to Language Assessment Part 1: Artificial Intelligence-Automated Scoring of a Sentence Recall Task for School-Age Children.

Authors

Dancheng Liu, Jason Yang, Carol A Miller, Pamela A Hadley, Sean M Redmond, Jinjun Xiong

Published in

Language, speech, and hearing services in schools. Pages 1-12. Oct 09, 2026. Epub Oct 09, 2026.

Abstract

Artificial intelligence (AI) technologies have the potential to enhance the delivery of speech-language pathology services. One area of practice that could benefit from AI is automated screenings for developmental language disorder (DLD) in early elementary students. This report presents the elements and processes involved in evaluating an AI-based automated scoring system using archival recordings.
A total of 947 audio samples of children completing the Redmond Sentence Recall (RSR) task, representing cases with and without DLD, were used. We developed an automated version of the RSR (AutoRSR), employing automated speech recognition (ASR), natural language processing, and machine learning models, to automate the tasks of transcribing recordings, tracking the number of errors children produced, scoring production accuracies based on RSR criteria, and generating pass/fail decisions based on age-referenced expectations against a reference standard (i.e., Core Language scores from the Clinical Evaluation of Language Fundamentals-Fourth Edition for a subset of 130 children). Different speech recognition models (Whisper and Reverb ASR) were applied to examine potential trade-offs. The accuracy of the AutoRSR was compared to human scoring.
AutoRSR with Whisper speech recognition yielded sensitivity = .768 and specificity = .784, whereas Reverb speech recognition yielded sensitivity = .873 and specificity = .730. While AutoRSR had some mismatches with human evaluators and tended to produce lower scores, it converged more closely with the reference standard than human-scored protocols.
AI technologies have become powerful enough to automate the scoring of recordings of children's sentence recall with similar accuracy levels as human evaluators. Next steps are discussed.
https://doi.org/10.23641/asha.34050876.

PMID:
42852990
Bibliographic data and abstract were imported from PubMed on 09 Oct 2026.

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