Preclinical Dialogue Simulation: Evaluating Response Accessibility in Conversational Artificial Intelligence for Aphasia Therapy.
Purpose: Large language model (LLM)-driven conversational agents are increasingly considered for use in clinical contexts, yet systematic approaches for evaluating their behavior in impairment-rich, speech-based therapeutic interactions remain limited. This study extends the Agent-Based Conversation...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 69; no. 8; pp. 3781 - 3797 |
|---|---|
| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
American Speech-Language-Hearing Association
Aug2026
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=196185582&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 196185582 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Aug2026 vid: 69 iid: 8 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 196185582 10.1044/2026_JSLHR-26-00031 ppf: 3781 ppct: 16 formats: fmt: @attributes: type: P size: 4.2MB tig: atl: Preclinical Dialogue Simulation: Evaluating Response Accessibility in Conversational Artificial Intelligence for Aphasia Therapy. aug: au: Imaezue, Gerald C. Maram, Krishna V. Ajayi, David Alohali, Isra Butta, Rajesh K. affil: Department of Communication Sciences and Disorders, University of South Florida, Tampa Department of Computer Science and Engineering, University of South Florida, Tampa su: Artificial intelligence Readability (Literary style) Communicative disorders Communication devices for people with disabilities Statistical models Rehabilitation of aphasic persons Benchmarking (Management) Research evaluation Natural language processing Speech-language pathology Simulation methods in education Data analysis software Speech therapy Regression analysis Sensitivity & specificity (Statistics) sug: subj: Artificial intelligence Readability (Literary style) Communicative disorders Communication devices for people with disabilities Statistical models Rehabilitation of aphasic persons Benchmarking (Management) Research evaluation Natural language processing Speech-language pathology Simulation methods in education Data analysis software Speech therapy Regression analysis Sensitivity & specificity (Statistics) ab: Purpose: Large language model (LLM)-driven conversational agents are increasingly considered for use in clinical contexts, yet systematic approaches for evaluating their behavior in impairment-rich, speech-based therapeutic interactions remain limited. This study extends the Agent-Based Conversational Dialogue (ABCD) simulation method as a preclinical testbed to evaluate how LLMs generate accessible clinician language when responding to characteristic aphasic speech during Response Elaboration Training. Method: ABCD was used to simulate multi-turn spoken therapeutic dialogues between an LLM-driven clinician and an artificial intelligence (AI)-simulated aphasic patient, enabling controlled manipulation of impairment profiles, prompting strategies, and reasoning modes without human participants. Three LLM families (Claude, GPT, and Gemini) were benchmarked under zero-shot and few-shot prompting and standard versus advanced reasoning. Response accessibility was quantified using established readability metrics (Flesch Reading Ease, Dale-Chall) and a composite score derived from 16 standardized readability measures. Results: Distinct accessibility signatures emerged across model architectures and configurations. Few-shot prompting and advanced reasoning generally yielded more accessible clinician responses, whereas Gemini demonstrated superior accessibility under zero-shot, standard reasoning. Conclusions: LLMs differ systematically in their capacity to adapt clinician language to impaired speech. ABCD provides a scalable, preclinical dialogue simulation framework for benchmarking conversational AI in clinically oriented, impairment-rich dialogue across multidimensional constraints. It offers guidance for model selection and configuration prior to clinical translation in communication rehabilitation. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|