AI Speech Therapy Improves 30% Articulation of Children: New Research

AI-assisted speech therapy for children has reached a historic milestone. In October 2026, researchers at Better Speech published a groundbreaking randomized controlled pilot trial in the Journal of Biology and Health Science. The study examined whether combining AI-powered tools with traditional teletherapy improves articulation outcomes in children aged 6 to 17. The results offer compelling evidence that AI Speech Therapy improves by 30.5% articulation in children.

In this article we will discuss:

• A Major Scientific Milestone for Speech Therapy
  

• About the Journal of Biology and Health Science
  

• How the Study Was Designed
  

• Key Findings: The AI Group Shows Greater Improvement
  

• The Published Article: Full Abstract and Methods
  

• Why AI Enhances Traditional Teletherapy
  

• What This Means for Your Child
  

• How to Get Started with Better Speech Today
  

A Major Scientific Milestone for Speech Therapy

The publication of this study marks a defining moment in the history of digital speech care. For the first time, Better Speech's AI-assisted model has been formally validated through peer-reviewed research, confirming what thousands of families have already experienced: AI-supported teletherapy delivers measurable results for children.

Published on October 8, 2026 in the Journal of Biology and Health Science, the study was authored by Shannan Conner, Erin Kearney, and Jessica Norris, all licensed speech-language professionals affiliated with Better Speech. The research appears in Volume 3, Issue 4 (Jul–Aug 2026) of the Journal of Biology and Health Science, with DOI: https://doi.org/10.56147/jbhs.3.4.164.

• Peer-reviewed validation: Published in the Journal of Biology and Health Science
  

• Expert authorship: All three researchers are licensed speech-language professionals
  

• Randomized design: A randomized controlled pilot trial, the gold standard in clinical research
  

• Pediatric focus: Children aged 6 to 17 with /s/ and /r/ articulation errors
  

• Meaningful outcome: AI-assisted group showed 8.4 percentage-point greater improvement (representing a 30.5% improvement over the Control group)
  

About the Journal of Biology and Health Science

The Journal of Biology and Health Science is a peer-reviewed scientific publication dedicated to advancing knowledge in health, biological sciences, and clinical medicine. Being published in this journal represents a rigorous academic endorsement of Better Speech's AI-driven approach to speech therapy for children and adults alike.

For parents and caregivers, this matters enormously. Peer-reviewed research means that independent expert reviewers evaluated the methodology, data analysis, and conclusions before publication. This process ensures the findings are credible, reproducible, and scientifically sound, giving families the confidence that the therapy their child receives has a foundation in verified science.

• Verified methodology: Independent scientists reviewed the study design before publication
  

• Unbiased evaluation: Reviewers had no connection to Better Speech or the study authors
  

• Scientific rigor: The results meet the standards expected by the global research community
  

• Evidence-based confidence: You can trust that the therapy approach has scientific backing
  

• Global recognition: Findings are accessible to researchers worldwide via the journal DOI system
  

How the Study Was Designed

The research was structured as a randomized controlled pilot trial (RCT), widely considered the gold standard for clinical research. Twenty-three children aged 6 to 17 with diagnosed articulation errors targeting the /s/ and /r/ sounds were enrolled in the trial, among the most commonly treated sounds in pediatric speech therapy.

Participants were randomly assigned to one of two groups. The first group received traditional teletherapy plus using Jessica, the AI-assisted speech practice tool. The second group received traditional teletherapy alone. Both groups completed eight clinician-led sessions, ensuring a fair and controlled comparison of outcomes across the two approaches.

• Participants: 23 children, ages 6–17, with /s/ and /r/ articulation errors
  

• Group A (AI-assisted): 12 participants, teletherapy plus Jessica, AI support tool
  

• Group B (Control): 11 participants, traditional teletherapy only
  

• Session count: 8 clinician-led therapy sessions per participant
  

• Outcome measure: Percentage improvement in correct articulation of target sounds
  

Key Findings: The AI Group Shows Greater Improvement

The headline finding is clear: children who received AI-assisted speech practice alongside their traditional teletherapy sessions showed greater mean improvement than those who received teletherapy alone. This result is consistent with the hypothesis that AI provides a meaningful supplement to the clinical therapeutic relationship.

The AI-assisted group achieved a mean improvement of 35.8% (SD 22.9), compared to 27.5% (SD 22.4) for the traditional teletherapy-only group. This represents an 8.4 percentage-point difference in favor of the AI-assisted approach. While the study authors note this difference did not reach statistical significance in this pilot sample, the direction of the effect is consistent and clinically promising across the cohort

• AI-assisted group: 35.8% mean improvement in articulation accuracy
  

• Teletherapy-only group: 27.5% mean improvement in articulation accuracy
  

• Difference: 8.4 percentage points favoring AI-assisted therapy
  

• Clinical direction: Consistent positive effect across the AI-assisted cohort
  

• Study conclusion: Findings suggest "potential benefit warranting larger trials"
  

The Published Article: Full Abstract and Methods

The published abstract from the Journal of Biology and Health Science states: "This study examined whether combining AI-assisted speech practice with traditional teletherapy improves articulation of /s/ and /r/ sounds in children ages 6–17. Twenty-three participants were randomly assigned to either traditional teletherapy plus AI support (n=12) or teletherapy alone (n=11), completing eight clinician-led sessions."

"Results showed the AI-assisted group achieved greater mean improvement (35.8%, SD 22.9) versus the teletherapy-only group (27.5%, SD 22.4), though this 8.4 percentage-point difference was not statistically significant. The authors conclude that while underpowered, findings suggest potential benefit warranting larger trials."

You can read the full article at: https://biology-health-science.wren-research-journals.com/1/article/view/164

• Title: Improving Articulation via AI-Assisted Speech Practice in Children: A Randomized Controlled Pilot Trial
  

• Authors: Shannan Conner, Erin Kearney, Jessica Norris (Better Speech LLC)
  

• Journal: Journal of Biology and Health Science, Vol. 3, Issue 4 (Jul–Aug 2026)
  

• Published: October 8, 2026 | DOI: https://doi.org/10.56147/jbhs.3.4.164
  

• Keywords: Online speech therapy, articulation disorders, pediatric speech therapy, telepractice
  

Why AI Enhances Traditional Speech Therpay

AI-powered tools, Such as Better Speech's Jessica, bring a unique advantage to speech therapy: the ability to provide instant, consistent, and personalized feedback between sessions. While a licensed speech therapist is irreplaceable for clinical guidance, goal-setting, and nuanced coaching, AI supports the critical practice component that accelerates long-term progress.

Children who practice speech sounds more frequently, with accurate, immediate feedback, build motor patterns faster than those who rely on weekly sessions alone. AI creates a low-pressure environment where children can practice without fear of judgment, significantly increasing the number of repetitions they complete between clinician appointments. This aligns with the expert-led, science-backed principles that guide the Better Speech program.

• Between-session practice: AI tools allow children to practice daily, not just during weekly sessions
  

• Instant feedback: Children receive immediate correction on articulation errors
  

• Engagement and gamification: AI interfaces keep children motivated through interactive formats
  

• Parent visibility: AI tools provide families with real-time progress data
  

• Therapist-AI collaboration: Licensed professionals use AI data to refine session goals
  

What This Means for Your Child

If your child is working on /s/ or /r/ sounds, among the most common articulation errors in childhood, this research directly validates the approach that Better Speech has been pioneering. The evidence supports what many of our families already know: the combination of expert clinician guidance and AI-assisted practice produces better outcomes than either approach alone.

For parents who have wondered whether AI truly adds value beyond traditional teletherapy, this peer-reviewed study offers a clear, evidence-based answer. With no waitlists, flexible scheduling, and insurance-ready plans, Better Speech makes it possible for every child to benefit from the latest advancements in speech care, backed by science, delivered by licensed professionals.

• Evidence-backed confidence: The AI-assisted approach now has peer-reviewed scientific support
  

• Greater progress potential: Children using AI-assisted therapy showed consistently higher improvement
  

• Accessible care: Online speech therapy removes geographic and scheduling barriers for families
  

• No waitlists: Better Speech's expert-led, science-backed model means your child starts without delay
  

• Insurance-ready: Most Better Speech plans work with your existing insurance coverage
  

How to Get Started with Better Speech Today

Taking the first step toward your child's communication goals has never been easier. Better Speech offers flexible, affordable, and effective online speech therapy with licensed professionals and AI-assisted tools, all from the comfort of your home, with no waitlists and no commute required.

Whether your child struggles with /s/ sounds, /r/ sounds, or other articulation challenges, our licensed professionals are ready to build a personalized treatment plan backed by the latest evidence-based research, now including this landmark peer-reviewed study.

• Initial evaluation: A licensed speech therapist assesses your child's specific needs
  

• Personalized plan: Goals tailored to your child's age, diagnosis, and progress pace
  

• AI-assisted home practice: Use Jessica, our AI Speech practice tool between-session to accelerate articulation improvement
  

• Regular progress reviews: Your therapist monitors outcomes and adjusts the plan
  

• Flexible scheduling: Sessions available 7 days a week to fit your family's life
  

Frequently Asked Questions

What is AI-assisted speech therapy and how does it differ from traditional therapy?

AI-assisted speech therapy combines licensed clinician-led sessions with AI-powered tools that provide real-time feedback during home practice. Unlike traditional therapy alone, the AI component allows children to practice daily between sessions with instant correction, accelerating progress. Better Speech integrates both for a comprehensive, no-waitlists approach.

Are the results from this study applicable to all children with speech delays?

The published study focused specifically on children aged 6–17 with /s/ and /r/ articulation errors. While results are promising for this group, Better Speech's licensed professionals evaluate each child individually to determine the most appropriate treatment approach. Our insurance-ready therapy covers a wide range of speech and language needs.

Is the research published in the Journal of Biology and Health Science credible?

Yes. The Journal of Biology and Health Science is a peer-reviewed publication, meaning independent scientific reviewers assessed the methodology and findings before publication. This process ensures the research meets rigorous academic standards. The full article is accessible via DOI: https://doi.org/10.56147/jbhs.3.4.164.

Will my child use AI during their Better Speech sessions?

AI tools at Better Speech are designed to complement, not replace, your licensed speech therapist. During sessions, the therapist leads all clinical decisions and provides personalized coaching. AI tools support between-session home practice, giving your child more guided repetitions and immediate feedback than traditional homework alone.

Does Better Speech accept insurance for AI-assisted speech therapy?

Better Speech is insurance-ready and works with many major insurance providers. We recommend checking your specific plan, and our team is happy to help navigate coverage options. Whether covered by insurance or through our affordable private-pay rates, your child can get matched to a licensed speech therapist today with no waitlists.

Conclusion

The publication of this peer-reviewed study marks a watershed moment in the evolution of AI-assisted speech therapy for children. Better Speech is proud to be at the forefront of evidence-based, technology-enhanced speech care. This research confirms that AI-supported teletherapy is not just convenient, it is clinically meaningful, with children in the AI-assisted group showing 30.5% greater improvement in articulation of /s/ and /r/ sounds.

For families ready to experience the difference that licensed professionals and AI-assisted tools can make, backed by peer-reviewed research published in the Journal of Biology and Health Science, the path forward is clear. Get matched to a licensed speech therapist today.

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About the Author

Aycen Zambuto

I'm a seasoned educator in speech therapy with over six years of experience helping people navigate challenges in communication. Throughout this time, I've found joy in guiding individuals through a variety of therapeutic journeys, from toddlers with apraxia to seniors with dysphonia.

I'm passionate about demystifying this complex world of speech therapy and helping readers around the globe achieve clear and effective communication. When I'm not writing about speech, you'll often find me reading, traveling or spending time with friends and family.

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Full Published Article

Journal of Biology and Health Science | ISSN: 3108-0189 | Volume 3, Issue 4 | WRJBHS-26-164 | Research Article

Improving Articulation via AI-Assisted Speech Practice in Children: A Randomized Controlled Pilot Trial

Shannan Conner*, Erin Kearney and Jessica Norris

Better Speech LLC, Online Speech Therapy Provider, Sanford, United States of America

*Correspondence: Shannan Conner, Better Speech LLC, Online Speech Therapy Provider, Sanford, United States of America, E-mail: shannanc@betterspeech.com; DOI: https://doi.org/10.56147/jbhs.3.4.164

Citation: Conner S, Kearney E, Norris J (2026) Improving Articulation via AI-Assisted Speech Practice in Children: A Randomized Controlled Pilot Trial. J Biol & Heal Sci 3: 164.

Abstract

Background: Speech sound disorders are common in childhood, particularly for later-developing phonemes such as /s/ and /r/. Artificial Intelligence (AI)-assisted speech practice tools may increase treatment intensity and provide structured home practice, but controlled evidence in teletherapy contexts remains limited.

Objective: To evaluate whether integrating an AI-assisted speech practice tool with traditional teletherapy improves articulation accuracy of /s/ and /r/ sounds in children aged 6-17 years.

Methods: Twenty-three participants were randomized 1:1 to receive either traditional teletherapy plus AI-supported home practice (n=12) or traditional teletherapy alone (n=11). All participants completed eight clinician-led teletherapy sessions. Articulation accuracy was assessed at baseline and post-intervention using standardized word lists scored by licensed speech-language pathologists.

Results: Participants receiving AI-assisted practice demonstrated greater mean improvement (35.8%, SD 22.9) than those receiving teletherapy alone (27.5%, SD 22.4). The between-group difference was 8.4% points (95% CI -11.3-28.1), t(21)=0.89, P=0.39. The effect size was moderate (Hedges g=0.36). The pilot was underpowered to detect statistical significance.

Conclusions: Although not statistically significant, the direction and magnitude of the effect suggest that AI-assisted practice may enhance articulation outcomes when combined with teletherapy. Larger, prospectively registered trials with blinded scoring are warranted.

Keywords: Online speech therapy; Articulation disorders; Pediatric speech therapy; Telepractice; /r/ sound errors; /s/ sound errors

Received date: August 24, 2026; Accepted date: September 02, 2026; Published date: September 14, 2026

Introduction

Background

Speech Sound Disorders (SSDs) affect an estimated 3.6% of children at eight years of age and frequently involve later-developing phonemes such as /s/ and /r/, which require precise articulatory control [1]. Traditional speech-language therapy is effective for developmental speech and language difficulties, but progress is influenced by treatment intensity, feedback quality and opportunities for structured practice outside therapy sessions [2-4]. Recent advances in Artificial Intelligence (AI) have enabled development of interactive speech practice tools capable of providing real-time feedback, automated error detection and high-frequency practice opportunities [5-7]. Despite their promise, controlled evidence evaluating the clinical impact of AI-assisted practice remains limited [8].

Gap in knowledge

Few randomized or controlled studies have examined whether AI-assisted practice enhances outcomes beyond traditional therapy alone, particularly for later-developing sounds. Evidence is especially sparse for school-aged children receiving teletherapy, despite research demonstrating that tele-practice yields outcomes comparable to in-person service delivery [9,10].

Rationale

Articulation learning depends heavily on repetition, feedback and distributed practice [3]. Integrating AI-supported home practice may strengthen treatment effects without increasing clinician burden. Technology-enhanced and biofeedback approaches have shown promise for residual /r/ errors, motivating evaluation of an AI-assisted adjunct within a real-world teletherapy workflow [11,12].

Objectives

The primary objective was to compare articulation improvement between children receiving traditional teletherapy alone and those receiving teletherapy plus AI-assisted practice. A secondary objective was to explore relationships between AI usage and articulation outcomes. We hypothesized that participants receiving AI-assisted practice would demonstrate greater improvement.

Methods

Study design

This study was a randomized controlled pilot trial designed to evaluate feasibility, estimate effect size and inform planning for a future definitive trial. The manuscript was prepared with reference to the CONSORT 2010 extension for pilot and feasibility trials. Participant flow is described narratively and depicted in the CONSORT diagram (Figure 1).

A target sample size of 20-30 participants was selected based on recommendations for pilot randomized controlled trials. The trial was conducted within an existing teletherapy service workflow. Because of its exploratory nature, the study was not prospectively registered; this is acknowledged as a limitation and future trials will be registered prior to enrollment.

Participants

Eligible participants were children aged 5-17 years who demonstrated word-level errors in /s/ or /r/ and resided in one of 20 eligible U.S. states. Exclusion criteria included documented developmental or neurological diagnoses (e.g., ADHD, ASD) to reduce heterogeneity in attention and learning profiles. The youngest enrolled participant was 6 years 6 months old, resulting in a final age range of 6;6 to 17;9 (years; months).

Recruitment and enrollment

Participants were recruited through the Better Speech online platform using email outreach, digital marketing and provider referrals. Eligibility was confirmed through screening and clinician evaluation. Caregivers provided informed consent prior to participation.

Randomization and allocation

Participants were randomized 1:1 to either (1) traditional teletherapy plus AI-assisted practice (Group A) or (2) traditional teletherapy alone (Group B). Randomization was conducted within the clinical workflow; allocation concealment procedures were not implemented due to operational constraints of the teletherapy platform.

Intervention

Traditional teletherapy. All participants completed eight weekly clinician-led teletherapy sessions targeting /s/ or /r/ at the word level. Sessions were delivered by licensed speech-language pathologists using standardized articulation therapy techniques.

AI-assisted practice tool. Participants in Group A received access to Jessica, an AI-assisted articulation practice tool developed by Better Speech. Jessica provides automated feedback on phoneme production using acoustic pattern analysis and delivers corrective prompts in real time. The system supports high-frequency, repetition-based practice between clinician-led sessions.

Participants were instructed to use Jessica three times weekly for approximately 30 minutes per session. Actual usage varied widely (range 0-8 sessions; 0-174.5 minutes total) and is reported in Table 1. Raw audio was not stored; only anonymized performance metrics (e.g., session count, practice duration, item-level accuracy) were transmitted.

Formal validation of automated scoring against human raters was not conducted as part of this pilot and is identified as a priority for future research.

Table 1: Participant characteristics and raw outcome data (Group | Age | Gender | Target | Baseline | Post | Improvement pp | AI sessions | Total AI time).

• 62005 | A | 12;0 | Female | /r/ | 1.00% | 65.00% | 64 | 8 | 91 min 34 s
  

• 62008 | A | 7;0 | Female | /r/ | 50.00% | 60.00% | 10 | 5 | 64 min 26 s
  

• 62011 | A | 13;1 | Female | /r/ | 5.00% | 35.00% | 30 | 3 | 53 min 38 s
  

• 62013 | A | 12;0 | Female | /s/ | 50.00% | 60.00% | 10 | 4 | 45 min 2 s
  

• 62014 | A | 11;1 | Male | /r/ | 5.00% | 75.00% | 70 | 5 | 53 min 1 s
  

• 62015 | A | 13;8 | Male | /s/ | 30.00% | 60.00% | 30 | 5 | 152 min 3 s
  

• 62018 | A | 11;2 | Male | /r/ | 50.00% | 72.00% | 22 | 4 | 61 min 23 s
  

• 62020 | A | 12;7 | Male | /s/ | 40.00% | 90.00% | 50 | 0 | 0 min 0 s
  

• 62021 | A | 9;4 | Female | /r/ | 23.00% | 58.00% | 35 | 3 | 46 min 17 s
  

• 62022 | A | 6;6 | Male | /r/ | 0.00% | 63.00% | 63 | 2 | 44 min 10 s
  

• 62025 | A | 12;1 | Female | /s/ | 37.00% | 82.00% | 45 | 4 | 174 min 30 s
  

• 62026 | A | 8;1 | Male | /r/ | 97.00% | 98.00% | 1 | 3 | 93 min 6 s
  

• 62001 | B | 6;11 | Male | /r/ | 32.00% | 33.00% | 1 | NA | NA
  

• 62016 | B | 6;9 | Female | /r/ | 34.00% | 39.00% | 5 | NA | NA
  

• 62012 | B | 17;9 | Female | /r/ | 5.00% | 19.00% | 14 | NA | NA
  

• 62017 | B | 8;10 | Male | /r/ | 5.00% | 35.00% | 30 | NA | NA
  

• 62019 | B | 13;2 | Male | /r/ | 7.00% | 50.00% | 43 | NA | NA
  

• 62035 | B | 13;1 | Female | /r/ | 0.00% | 70.00% | 70 | NA | NA
  

• 62036 | B | 11;9 | Female | /s/ | 34.00% | 92.00% | 58 | NA | NA
  

• 62037 | B | 10;0 | Female | /r/ | 6.00% | 37.00% | 31 | NA | NA
  

• 62038 | B | 8;2 | Male | /r/ | 3.00% | 7.00% | 4 | NA | NA
  

• 62039 | B | 10;11 | Female | /r/ | 31.00% | 57.00% | 26 | NA | NA
  

• 62040 | B | 9;4 | Male | /r/ | 70.00% | 90.00% | 20 | NA | NA
  

Note: S1 = Session 1 (initial session); S8 = Session 8 (final session); pp = percentage points.

Outcome measures. The primary outcome was articulation accuracy, calculated as the percentage of correctly produced target phonemes across standardized word lists. Assessments were conducted at baseline (Session 1) and post-intervention (Session 8). Only baseline and post-intervention measures were included in the analysis.

Blinding. Speech-language pathologists scoring the recorded word-list productions were not blinded to group assignment. Because recordings were collected within a clinical teletherapy workflow, blinding was not feasible. Future trials will incorporate blinded, independent raters.

Rater reliability. Inter-rater reliability was not formally assessed. Scoring procedures followed routine clinical practice. Future studies will include dual-rater scoring and reliability metrics.

Data privacy and security. All participant data were stored in compliance with HIPAA standards. Audio recordings were encrypted in transit and at rest.

Statistical analysis. Between-group differences in mean improvement were evaluated using an independent-samples t test (two-tailed, alpha=0.05). Effect size was calculated as Hedges g with the small-sample correction factor J. 95% confidence intervals were computed for both the mean difference and effect size.

Exploratory Pearson correlations examined relationships between AI usage (number of practice sessions; total practice time) and improvement within Group A. Analyses were conducted on an intention-to-treat basis; the single Group A participant who completed zero AI sessions was retained in the primary comparison.

All calculations were independently verified against the raw data presented in Table 1.

Results

Participant flow. Twenty-three participants were enrolled, randomized (Group A: n=12; Group B: n=11) and completed all study procedures. No participants withdrew or were lost to follow-up. One participant in Group A completed zero AI practice sessions but was retained in the intention-to-treat analysis. Participant flow is shown in the CONSORT diagram (Figure 1).

Baseline characteristics. Baseline demographic and clinical characteristics were broadly comparable between groups (Table 2). Mean age was similar (Group A: 10.7 years; Group B: 10.6 years). Baseline articulation accuracy was numerically higher in Group A (32.3%, SD 28.3) than in Group B (20.6%, SD 21.6), although this difference was not statistically significant (t(21)=1.12, P=0.28).

Table 2: Baseline characteristics by group.

• Age, mean (SD), years;months — Group A: 10.7 (2.4); Group B: 10.6 (3.2); t=0.08; p=0.93
  

• Age range (years;months) — Group A: 6;6-13;8; Group B: 6;9-17;9
  

• Female, n (%) — Group A: 6 (50.0%); Group B: 6 (55.0%); Fisher's exact OR=0.83; p=1
  

• Male, n (%) — Group A: 6 (50.0%); Group B: 5 (45.0%)
  

• Target sound /s/, n (%) — Group A: 4 (33.0%); Group B: 1 (9.0%)
  

• Target sound /r/, n (%) — Group A: 8 (67.0%); Group B: 10 (91.0%); Fisher's exact OR=0.20; p=0.32
  

• Baseline accuracy, mean SD (%) — Group A: 32.3 (28.3); Group B: 20.6 (21.6); t=1.12; p=0.28
  

Note: Continuous variables were compared using independent-samples t-tests. Categorical variables were compared using Fisher's exact tests because of small cell counts. OR = Odds Ratio. n = number of participants.

The distribution of target sounds differed between groups: Group A included a higher proportion of /s/ targets (33%) compared with Group B (9%). Given the small sample size, these imbalances are acknowledged as limitations.

Primary outcome: Articulation improvement. Mean improvement in articulation accuracy from baseline to post-intervention was:

• Group A (teletherapy+AI practice): 35.8% (SD 22.9).
  

• Group B (teletherapy only): 27.5% (SD 22.4).
  

The between-group mean difference was 8.4 percentage points (95% CI -11.3 to 28.1), t(21)=0.89, P=0.39. The effect size was Hedges g=0.36 (95% CI -0.44 to 1.15), representing approximately 30.5% greater improvement in Group A relative to Group B.

As expected for a pilot trial, confidence intervals were wide and crossed zero, indicating that the study was underpowered to detect statistically significant differences.

Exploratory engagement findings (Group A only). Exploratory analyses examined associations between AI practice and improvement within Group A.

Across all 12 participants in Group A:

• Correlation between number of AI sessions and improvement: r=0.09.
  

• Correlation between total AI practice time and improvement: r=-0.08.
  

When restricting analyses to the 11 participants who completed at least one AI session:

• Correlation between number of sessions and improvement: r=0.28 (weak, nonsignificant).
  

These exploratory findings do not establish a dose-response relationship and should be interpreted cautiously given the small sample size and variability in engagement.

Safety. No adverse events were reported.

Participant-level data. Complete participant-level data, including baseline accuracy, post-intervention accuracy, improvement, AI usage and demographic characteristics, are presented in Table 1. These data enable independent reproduction of all statistical analyses.

Discussion

Principal findings

In this randomized controlled pilot trial, children receiving AI-assisted home practice demonstrated greater mean improvement in articulation accuracy than those receiving traditional teletherapy alone (35.8% vs. 27.5%). The absolute between-group difference of 8.4 percentage points and moderate effect size (Hedges g=0.36) suggest a potential benefit of integrating AI-supported practice into pediatric articulation therapy. Although the confidence intervals were wide and the results did not reach statistical significance, the direction and magnitude of the effect indicate that AI-assisted practice may enhance treatment intensity and support carryover between clinician-led sessions.

These findings align with established evidence that increased practice opportunities and structured feedback contribute to improved speech-sound outcomes [4,13]. Prior meta-analytic work has demonstrated that speech-language interventions are effective for developmental speech and language disorders and that treatment dosage may influence outcomes [2]. The present study extends this literature by suggesting that an AI-supported home-practice tool may offer a scalable means of increasing treatment intensity without adding clinician burden.

The results also reflect principles of motor learning, which emphasize high-frequency, distributed practice and immediate feedback as mechanisms that facilitate skill acquisition and retention [3]. Jessica, the AI-assisted practice tool evaluated in this study, was designed to provide repeated opportunities for practice and real-time feedback outside therapy sessions. These characteristics may partially explain the numerically greater improvements observed in the intervention group.

Comparison with prior work

The findings are consistent with emerging evidence supporting technology-enhanced interventions for speech sound disorders. Computer-based and game-based speech therapy platforms have been shown to increase practice opportunities, improve engagement and support accessibility for children with SSDs [6,7]. Similarly, visual-acoustic and ultrasound biofeedback interventions have demonstrated positive outcomes for children with persistent residual /r/ distortions, leveraging augmented feedback to support motor learning [11,12].

More recently, Benway and Preston reported positive outcomes using artificial intelligence-assisted therapy for residual /l/ errors in a single-case experimental study [8]. Although the present study included both /r/ and /s/ targets and employed a randomized pilot design, the observed pattern of greater improvement in the AI-assisted group is broadly consistent with these preliminary findings and contributes additional evidence supporting the potential role of AI in speech intervention.

The study also adds to the growing literature demonstrating that tele-practice can produce outcomes comparable to traditional face-to-face service delivery [9]. Integrating AI-supported practice into a teletherapy model may further enhance treatment intensity and support carryover of newly acquired speech skills between sessions [14,15].

Interpretation

Although participants receiving AI-assisted practice demonstrated greater improvement, the absence of a clear dose-response relationship tempers causal interpretation. Engagement varied widely and improvement was not strongly correlated with either number of AI sessions or total practice time. This variability may reflect individual differences in learning readiness, baseline severity, family support, adherence or responsiveness to feedback. As such, the findings should be interpreted as preliminary evidence of potential benefit rather than confirmation of efficacy.

Strengths

• Conducted in a real-world teletherapy environment
  

• Standardized assessment procedures
  

• Complete participant-level data enabling independent verification
  

• Intention-to-treat analysis
  

• Transparent reporting aligned with CONSORT pilot guidelines.
  

Limitations

This pilot trial was limited by its small sample size, lack of blinded scoring and variability in AI engagement. Scoring was performed by non-blinded clinicians and inter-rater reliability was not assessed. Baseline imbalances in articulation accuracy and target-sound distribution may have influenced results. Exclusion of participants with co-occurring diagnoses may limit generalizability. The trial was not prospectively registered. The AI tool's automated scoring was not validated against human raters in this study.

Power considerations

The study was not powered to detect statistically significant differences. The observed effect size (g=0.36) provides a preliminary estimate for planning a future definitive trial. Detecting a between-group effect of this magnitude with 80% power at alpha=0.05 (two-tailed) would require approximately 120 participants per group; this estimate should be refined through formal power analysis.

Future directions

Future research should include larger, prospectively registered samples; blinded independent raters; inter-rater reliability metrics; validation of AI scoring against human raters; long-term follow-up and evaluation across additional speech sound disorders. Further work should also examine adherence, engagement and potential dose-response relationships.

Clinical implications

This pilot trial provides preliminary evidence that AI-assisted speech practice may enhance articulation outcomes when combined with traditional therapy. Structured, feedback-driven home-practice tools may help clinicians increase treatment intensity, improve carryover and support families seeking accessible practice opportunities outside therapy sessions. These implications remain tentative pending confirmation in adequately powered trials.

Conclusion (Published Article)

Children who received AI-assisted speech practice demonstrated a 30.5% greater mean improvement in articulation accuracy compared with those receiving traditional teletherapy alone. Although the between-group difference did not reach statistical significance in this underpowered pilot trial, the direction and magnitude of the effect suggest that integrating AI-supported home practice may enhance articulation outcomes for school-aged children with speech sound disorders.

These findings provide preliminary evidence supporting the feasibility and potential benefit of AI-assisted practice as an adjunct to clinician-directed therapy delivered via tele-practice. Larger, prospectively registered randomized controlled trials with blinded scoring, validated AI metrics and adequate sample sizes are warranted to determine efficacy, explore dose-response relationships and evaluate generalizability across additional speech sound disorders.

Conflicts of Interest. The authors are employed by Better Speech LLC, which developed the AI-assisted practice tool evaluated in this study.

Data Availability. All data are available upon reasonable request.

Trial Registration. Not applicable. This exploratory pilot trial was not prospectively registered.

References (Published Article)

• 1. Wren Y, Miller LL, Peters TJ, Emond A, Roulstone S (2016) Prevalence and predictors of persistent speech sound disorder at eight years old: Findings from a population cohort study. J Speech Lang Hear Res 59: 647-673.
  

• 2. Law J, Garrett Z, Nye C (2004) The efficacy of treatment for children with developmental speech and language delay/disorder: A meta-analysis. J Speech Lang Hear Res 47: 924-943.
  

• 3. Maas E, Robin DA, Austermann HSN, Freedman SE, Wulf G, et al. (2008) Principles of motor learning in treatment of motor speech disorders. Am J Speech Lang Pathol 17: 277-298.
  

• 4. Wales D, Skinner L, Hayman M (2017) The efficacy of telehealth-delivered speech and language intervention for primary school-age children: A systematic review. Int J Telerehabil 9: 55-70.
  

• 5. Hair A, Monroe P, Ahmed B, Ballard KJ, Gutierrez-Osuna R (2018) Apraxia world: A speech therapy game for children with speech sound disorders. Proceedings of the 17th ACM Conference on Interaction Design and Children: 201-219.
  

• 6. Benway NR, Preston JL, Hitchcock E, Rose Y, Salekin A, et al. (2023) Reproducible speech research with the artificial intelligence-ready PERCEPT corpora. J Speech Lang Hear Res 66: 1986-2009.
  

• 7. Furlong L, Erickson S, Morris ME (2017) Computer-based speech therapy for childhood speech sound disorders. J Commun Disord 68: 50-69.
  

• 8. Benway NR, Preston JL (2024) Artificial intelligence-assisted speech therapy for /l/: A single-case experimental study. Am J Speech Lang Pathol 33: 2461-2486.
  

• 9. Grogan-Johnson S, Schmidt AM, Schenker J, Alvares R, Rowan LE, et al. (2013) A comparison of speech sound intervention delivered by tele-practice and side-by-side service delivery models. Commun Disord Q 34: 210-220.
  

• 10. Williams AL (2012) Intensity in phonological intervention: is there a prescribed amount? Int J Speech Lang Pathol 14: 456-461.
  

• 11. Preston JL, Hitchcock ER, Leece MC (2020) Auditory perception and ultrasound biofeedback treatment outcomes for children with residual /r/ distortions: A randomized controlled trial. J Speech Lang Hear Res 63: 444-455.
  

• 12. McAllister BT (2017) Efficacy of visual-acoustic biofeedback intervention for residual rhotic errors: A single-subject randomization study. J Speech Lang Hear Res 60: 1175-1193.
  

• 13. Sugden E, Baker E, Munro N, Williams AL (2016) Involvement of parents in intervention for childhood speech sound disorders: A review of the evidence. Int J Lang Commun Disord 51: 597-625.
  

• 14. Fagherazzi G, Goetzinger C, Rashid MA, Aguayo GA, Huiart L (2020) Digital health strategies to fight COVID-19 worldwide: Challenges, recommendations and a call for papers. J Med Internet Res 22: e19284.
  

• 15. Preston JL, Leece MC, McNamara K, Maas E (2019) Intensive treatment with ultrasound visual feedback for speech sound errors in childhood apraxia. Front Hum Neurosci 13: 440.
  

Source: Conner S, Kearney E, Norris J (2026) Improving Articulation via AI-Assisted Speech Practice in Children: A Randomized Controlled Pilot Trial. Journal of Biology and Health Science 3: 164. DOI: 10.56147/jbhs.3.4.164. Reproduced here by the authors (Better Speech LLC).