Artificial Intelligence–Assisted Speech Therapy for /ɹ/: A Single-Case Experimental Study
An AI chaining app delivered at home sharply raised teens’ /r/ accuracy and carried over to new words.
01Research in Context
What this study did
Benway et al. (2024) tested an AI app that gives live feedback on the /r/ sound.
Kids used the app at home for 40-minute sessions.
The team tracked accuracy with a multiple-baseline design across participants.
What they found
Every teen hit better /r/ scores during and after the sessions.
Untrained words also sounded clearer at post-test, showing real carry-over.
How this fits with other research
Strömbergsson et al. (2026) ran a similar home-app setup and saw almost no gains.
The key gap: their app gave simple scores, while Benway’s AI chained tiny motor steps with instant sound cues.
MHeald et al. (2020) already proved AI can judge speech like a human; Benway moves that judge into the teaching chair.
BURNSTEIN et al. (1964) showed chaining boosts behavior sixty years ago—this study just lets a phone do the prompting.
Why it matters
If a client struggles with /r/, you can add ChainingAI sessions as homework.
The app keeps the trial rate high and gives sensor feedback you can’t provide over telehealth.
One caution: the teen still needs your baseline assessment and periodic probes to be sure the new /r/ holds in conversation.
Want CEUs on This Topic?
The ABA Clubhouse has 60+ free CEUs — live every Wednesday. Ethics, supervision & clinical topics.
Join Free →Map the child’s current /r/ contexts, then assign one 40-min ChainingAI session as homework and track generalization probes each week.
02At a glance
03Original abstract
This feasibility trial describes changes in rhotic production in residual speech sound disorder following ten 40-min sessions including artificial intelligence (AI)-assisted motor-based intervention with ChainingAI, a version of Speech Motor Chaining that predicts clinician perceptual judgment using the PERCEPT-R Classifier (Perceptual Error Rating for the Clinical Evaluation of Phonetic Targets). The primary purpose is to evaluate /ɹ/ productions directly after practice with ChainingAI versus directly before ChainingAI and to evaluate how the overall AI-assisted treatment package may lead to perceptual improvement in /ɹ/ productions compared to a no-treatment baseline phase. Five participants ages 10;7–19;3 (years;months) who were stimulable for /ɹ/ participated in a multiple (no-treatment)-baseline ABA single-case experiment. Prepractice activities were led by a human clinician, and drill-based motor learning practice was automated by ChainingAI. Study outcomes were derived from masked expert listener perceptual ratings of /ɹ/ from treated and untreated utterances recorded during baseline, treatment, and posttreatment sessions. Listeners perceived significantly more rhoticity in practiced utterances after 30 min of ChainingAI, without a clinician, than directly before ChainingAI. Three of five participants showed significant generalization of /ɹ/ to untreated words during the treatment phase compared to the no-treatment baseline. All five participants demonstrated statistically significant generalization of /ɹ/ to untreated words from pretreatment to posttreatment. PERCEPT-clinician rater agreement (i.e., F1 score) was largely within the range of human–human agreement for four of five participants. Survey data indicated that parents and participants felt hybrid computerized–clinician service delivery could facilitate at-home practice. This study provides evidence of participant improvement for /ɹ/ in untreated words in response to an AI-assisted treatment package. The continued development of AI-assisted treatments may someday mitigate barriers precluding access to sufficiently intense speech therapy for individuals with speech sound disorders. https://doi.org/10.23641/asha.26662807
American Journal of Speech-Language Pathology, 2024 · doi:10.1044/2024_AJSLP-23-00448