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Related Concept Videos

Therapeutic Communication01:30

Therapeutic Communication

Communication is a lifelong learning process. Through therapeutic communication, nurses can collect relevant assessment data, provide education and counseling, and interact during nursing interventions. Sending and receiving messages occur through verbal and nonverbal communication techniques and can happen separately or simultaneously.
Verbal communication depends on language or a prescribed way of using words so that people can share information effectively. The critical aspects of verbal...
Techniques of Therapeutic Communication II: Focusing, Paraphrasing, and Summarizing01:23

Techniques of Therapeutic Communication II: Focusing, Paraphrasing, and Summarizing

Focusing involves centering a conversation on a message's critical elements or concepts. Focusing is valuable if the talk is vague or patients begin to repeat themselves. Sometimes, when patients are asked about their symptoms, they may go off-topic and try to tell their entire life story. Respectfully, the nurse should bring the conversation back into focus.
This therapeutic technique can also be used when a patient brings up pertinent information during a health-related conversation. The...
Modeling in Therapy01:26

Modeling in Therapy

Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in situations...

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Related Experiment Video

Updated: Jun 26, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Preclinical Dialogue Simulation: Evaluating Response Accessibility in Conversational Artificial Intelligence for

Gerald C Imaezue1, Krishna V Maram2, David Ajayi1

  • 1Department of Communication Sciences and Disorders, University of South Florida, Tampa.

Journal of Speech, Language, and Hearing Research : JSLHR
|June 24, 2026
PubMed
Summary

Large language models (LLMs) show varied ability to generate accessible clinician language for aphasic speech. The Agent-Based Conversational Dialogue (ABCD) simulation method offers a preclinical testbed for evaluating these AI systems.

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Assessment and Communication for People with Disorders of Consciousness
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Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Area of Science:

  • Natural Language Processing
  • Clinical Linguistics
  • Artificial Intelligence in Healthcare

Background:

  • Conversational agents driven by large language models (LLMs) are being explored for clinical applications.
  • Systematic evaluation methods for LLM behavior in speech-based therapeutic interactions with impaired speech are limited.
  • Response Elaboration Training (RET) is a key therapeutic interaction for aphasia.

Purpose of the Study:

  • To extend the Agent-Based Conversational Dialogue (ABCD) simulation method as a preclinical testbed.
  • To evaluate how LLMs generate accessible clinician language when responding to simulated aphasic speech during RET.
  • To benchmark different LLM families (Claude, GPT, Gemini) and configurations.

Main Methods:

  • Simulated multi-turn spoken therapeutic dialogues between an LLM-clinician and an AI-simulated aphasic patient using the ABCD framework.
  • Controlled manipulation of impairment profiles, prompting strategies (zero-shot, few-shot), and reasoning modes (standard, advanced).
  • Quantification of response accessibility using established readability metrics and a composite score.

Main Results:

  • Distinct accessibility patterns emerged across LLM architectures and configurations.
  • Few-shot prompting and advanced reasoning generally improved response accessibility.
  • Gemini exhibited superior accessibility under zero-shot, standard reasoning conditions.

Conclusions:

  • LLMs demonstrate systematic differences in adapting clinician language for impaired speech.
  • The ABCD framework provides a scalable, preclinical simulation for benchmarking conversational AI in clinical dialogue.
  • Findings offer guidance for selecting and configuring LLMs prior to clinical deployment in communication rehabilitation.