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.
Studieoversigt
Status
Betingelser
Undersøgelsestype
Tilmelding (Faktiske)
Kontakter og lokationer
Studiesteder
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Istanbul
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Istanbul, Istanbul, Tyrkiet (Türkiye), 34310
- Istanbul Gelisim University
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Deltagelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
Tager imod sunde frivillige
Køn, der er berettiget til at studere
Prøveudtagningsmetode
Studiebefolkning
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.
Beskrivelse
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.
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
- Observationsmodeller: OTHER
- Tidsperspektiver: CROSS_SECTIONAL
Kohorter og interventioner
Gruppe / kohorte |
Intervention / Behandling |
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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. |
Hvad måler undersøgelsen?
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
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Expert-Rated Quality of Large Language Model Responses
Tidsramme: 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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Samarbejdspartnere og efterforskere
Sponsor
Datoer for undersøgelser
Studer store datoer
Studiestart (Faktiske)
Primær færdiggørelse (Faktiske)
Studieafslutning (Faktiske)
Datoer for studieregistrering
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
- LLM-STUTTER-2026
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