Large Language Models for Stuttering Assessment and Therapy
Alignment of Large Language Model Responses on Stuttering Definition, Assessment, and Therapy With Clinical Practice Guidelines: A Multi-Model Comparison
This observational study aims to compare the quality of responses generated by four large language models (ChatGPT, Claude, Google Gemini, and Grok-4.20) to questions about stuttering. The study focuses on three main areas: general information about stuttering, clinical assessment, and therapy. A total of nine questions were developed based on evidence-based clinical practice guidance, including the American Speech-Language-Hearing Association (ASHA) Practice Portal. Each question is presented to each language model in separate sessions, and the generated responses are recorded for evaluation.
Five speech-language therapists with clinical experience in stuttering independently evaluate the model-generated responses. Each response is rated for relevance, accuracy, clarity, completeness, and consistency using a 5-point Likert scale. The responses are also compared with guideline-based reference information. The study does not involve any clinical intervention or treatment of patients. The aim is to determine how closely large language model responses align with current clinical practice guidance and to identify their potential strengths and limitations when used as informational or clinical support tools in the field of stuttering.
Przegląd badań
Status
Status
Warunki
Warunki
Interwencja / Leczenie
Interwencja / Leczenie
Typ studiów
Typ studiów
Zapisy (Rzeczywisty)
Zapisy
Kontakty i lokalizacje
Lokalizacje studiów
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Istanbul
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Istanbul, Istanbul, Turcja (Türkiye), 34310
- Istanbul Gelisim University
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Kryteria uczestnictwa
Kryteria kwalifikacji
Kryteria kwalifikacji
Wiek uprawniający do nauki
Akceptuje zdrowych ochotników
Płeć kwalifikująca się do nauki
Metoda próbkowania
Badana populacja
The study population consisted of adult speech-language therapists in Türkiye who were actively working in the field of stuttering and fluency disorders. Five speech-language therapists with clinical experience in stuttering were purposively selected to independently evaluate responses generated by four large language models to standardized questions concerning stuttering information, assessment, and therapy.
Opis
Inclusion Criteria:
- Having an undergraduate or postgraduate degree in speech and language therapy.
- Actively practicing clinically in the field of stuttering/fluency disorders.
- Voluntarily agreeing to participate in the study.
- Completing the evaluation form in full.
Exclusion Criteria:
- Not having active clinical experience in the field of stuttering.
- Incomplete completion of the evaluation form.
- Withdrawal of voluntary participation during the evaluation process.
Plan studiów
Jak projektuje się badanie?
Szczegóły projektu
- Modele obserwacyjne: OTHER
- Perspektywy czasowe: CROSS_SECTIONAL
Liczba grup / kohort
Kohorty i interwencje
Grupa / KohortaGrupa / Kohorta |
Interwencja / LeczenieInterwencja / Leczenie |
|---|---|
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Expert Speech-Language Therapists
Five speech-language therapists with clinical experience in stuttering independently evaluated responses generated by four large language models (ChatGPT, Claude, Google Gemini, and Grok-4.20). The responses addressed nine questions covering general information about stuttering, clinical assessment, and therapy. Each response was rated for relevance, accuracy, clarity, completeness, and consistency using a 5-point Likert scale. |
ChatGPT responses to nine standardized questions on stuttering information, assessment, and therapy were generated in separate sessions and recorded for independent expert evaluation. Questions were repeated under comparable conditions to allow assessment of response consistency. Claude responses to nine standardized questions on stuttering information, assessment, and therapy were generated in separate sessions and recorded for independent expert evaluation. Questions were repeated under comparable conditions to allow assessment of response consistency. Google Gemini responses to nine standardized questions on stuttering information, assessment, and therapy were generated in separate sessions and recorded for independent expert evaluation. Questions were repeated under comparable conditions to allow assessment of response consistency. Grok-4.20 responses to nine standardized questions on stuttering information, assessment, and therapy were generated in separate sessions and recorded for independent expert evaluation. Questions were repeated under comparable conditions to allow assessment of response consistency. |
Co mierzy badanie?
Podstawowe miary wyniku
Podstawowe miary wyniku
Miara wyniku |
Opis środka |
Ramy czasowe |
|---|---|---|
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Expert-Rated Quality of Large Language Model Responses
Ramy czasowe: During the single cross-sectional expert evaluation conducted over approximately 1 month
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Responses generated by four large language models (ChatGPT, Claude, Google Gemini, and Grok-4.20) to nine standardized questions about stuttering were independently evaluated by five speech-language therapists with clinical experience in stuttering. Each response was rated using a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) across five criteria: relevance, accuracy, clarity, completeness, and consistency. Responses were evaluated against guideline-based reference information derived from evidence-based clinical practice guidance. Higher scores indicate better response quality and closer alignment with clinical practice guidance. Mean scores were calculated for each large language model and each evaluation criterion. |
During the single cross-sectional expert evaluation conducted over approximately 1 month
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Współpracownicy i badacze
Sponsor
Sponsor
Daty zapisu na studia
Główne daty studiów
Rozpoczęcie studiów (Rzeczywisty)
Rozpoczęcie studiów
Zakończenie podstawowe (Rzeczywisty)
Zakończenie podstawowe
Ukończenie studiów (Rzeczywisty)
Ukończenie studiów
Daty rejestracji na studia
Pierwszy przesłany
Pierwszy przesłany
Pierwszy przesłany, który spełnia kryteria kontroli jakości
Pierwszy przesłany, który spełnia kryteria kontroli jakości
Pierwszy wysłany (Rzeczywisty)
Pierwszy wysłany
Aktualizacje rekordów badań
Ostatnia wysłana aktualizacja (Rzeczywisty)
Ostatnia wysłana aktualizacja
Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości
Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości
Ostatnia weryfikacja
Ostatnia weryfikacja
Więcej informacji
Terminy związane z tym badaniem
Dodatkowe istotne warunki MeSH
Inne numery identyfikacyjne badania
Inne numery identyfikacyjne badania
- LLM-STUTTER-2026
Informacje o lekach i urządzeniach, dokumenty badawcze
Bada produkt leczniczy regulowany przez amerykańską FDA
Bada produkt urządzenia regulowany przez amerykańską FDA
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