Exploring the Efficacy of Robotic Assistants with ChatGPT and Claude in Enhancing ADHD Therapy: Innovating Treatment Paradigms
- URL: http://arxiv.org/abs/2406.15198v1
- Date: Fri, 21 Jun 2024 14:38:25 GMT
- Title: Exploring the Efficacy of Robotic Assistants with ChatGPT and Claude in Enhancing ADHD Therapy: Innovating Treatment Paradigms
- Authors: Santiago Berrezueta-Guzman, Mohanad Kandil, María-Luisa Martín-Ruiz, Iván Pau-de-la-Cruz, Stephan Krusche,
- Abstract summary: We integrate two advanced language models, ChatGPT-4 Turbo and Claude-3 Opus, into a robotic assistant to explore how well each model performs in robot-assisted interactions.
The results of this study show that ChatGPT-4 Turbo excelled in performance and responsiveness, making it suitable for time-sensitive applications.
- Score: 0.7201938834736084
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition characterized by inattention, hyperactivity, and impulsivity, which can significantly impact an individual's daily functioning and quality of life. Occupational therapy plays a crucial role in managing ADHD by fostering the development of skills needed for daily living and enhancing an individual's ability to participate fully in school, home, and social situations. Recent studies highlight the potential of integrating Large Language Models (LLMs) like ChatGPT and Socially Assistive Robots (SAR) to improve psychological treatments. This integration aims to overcome existing limitations in mental health therapy by providing tailored support and adapting to the unique needs of this sensitive group. However, there remains a significant gap in research exploring the combined use of these advanced technologies in ADHD therapy, suggesting an opportunity for novel therapeutic approaches. Thus, we integrated two advanced language models, ChatGPT-4 Turbo and Claude-3 Opus, into a robotic assistant to explore how well each model performs in robot-assisted interactions. Additionally, we have compared their performance in a simulated therapy scenario to gauge their effectiveness against a clinically validated customized model. The results of this study show that ChatGPT-4 Turbo excelled in performance and responsiveness, making it suitable for time-sensitive applications. Claude-3 Opus, on the other hand, showed strengths in understanding, coherence, and ethical considerations, prioritizing safe and engaging interactions. Both models demonstrated innovation and adaptability, but ChatGPT-4 Turbo offered greater ease of integration and broader language support. The selection between them hinges on the specific demands of ADHD therapy.
Related papers
- Integrating Reinforcement Learning and AI Agents for Adaptive Robotic Interaction and Assistance in Dementia Care [5.749791442522375]
This study explores a novel approach to advancing dementia care by integrating socially assistive robotics, reinforcement learning (RL), large language models (LLMs), and clinical domain expertise within a simulated environment.
arXiv Detail & Related papers (2025-01-28T06:38:24Z) - Task-Based Role-Playing VR Game for Supporting Intellectual Disability Therapies [2.4150871564195007]
Space Exodus is a task-based role-playing Virtual Reality (VR) game designed to support therapy for children with Intellectual Disability (ID)
Game integrates everyday life scenarios into an immersive environment to enhance skill acquisition and transfer.
Results provide empirical evidence supporting VR as a promising tool in ID therapy.
arXiv Detail & Related papers (2024-12-16T09:46:00Z) - Transparent but Powerful: Explainability, Accuracy, and Generalizability in ADHD Detection from Social Media Data [0.0]
Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental health condition affecting both children and adults, yet it remains severely underdiagnosed.
Recent advances in artificial intelligence, particularly in Natural Language Processing (NLP) and Machine Learning (ML), offer promising solutions for scalable and non-invasive ADHD screening methods using social media data.
This paper presents a comprehensive study on ADHD detection, leveraging both shallow machine learning models and deep learning approaches, to analyze linguistic patterns in ADHD-related social media text.
arXiv Detail & Related papers (2024-11-23T15:26:01Z) - CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy [67.23830698947637]
We propose a new benchmark, CBT-BENCH, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance.
We include three levels of tasks in CBT-BENCH: I: Basic CBT knowledge acquisition, with the task of multiple-choice questions; II: Cognitive model understanding, with the tasks of cognitive distortion classification, primary core belief classification, and fine-grained core belief classification; III: Therapeutic response generation, with the task of generating responses to patient speech in CBT therapy sessions.
Experimental results indicate that while LLMs perform well in reciting CBT knowledge, they fall short in complex real-world scenarios
arXiv Detail & Related papers (2024-10-17T04:52:57Z) - Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapy [6.952909762512736]
We examine the effects of prompt engineering to guide Large Language Models (LLMs) in delivering parts of a Problem-Solving Therapy session via text.
We demonstrate that the models' capability to deliver protocolized therapy can be improved with the proper use of prompt engineering methods.
arXiv Detail & Related papers (2024-08-27T17:25:16Z) - Large Language Model-based Human-Agent Collaboration for Complex Task
Solving [94.3914058341565]
We introduce the problem of Large Language Models (LLMs)-based human-agent collaboration for complex task-solving.
We propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC.
This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process.
arXiv Detail & Related papers (2024-02-20T11:03:36Z) - Evaluating the Efficacy of Interactive Language Therapy Based on LLM for
High-Functioning Autistic Adolescent Psychological Counseling [1.1780706927049207]
This study investigates the efficacy of Large Language Models (LLMs) in interactive language therapy for high-functioning autistic adolescents.
LLMs present a novel opportunity to augment traditional psychological counseling methods.
arXiv Detail & Related papers (2023-11-12T07:55:39Z) - Large Language Models Understand and Can be Enhanced by Emotional
Stimuli [53.53886609012119]
We take the first step towards exploring the ability of Large Language Models to understand emotional stimuli.
Our experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts.
Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks.
arXiv Detail & Related papers (2023-07-14T00:57:12Z) - Design, Development, and Evaluation of an Interactive Personalized
Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises [68.37238218842089]
We develop an interactive social robot exercise coaching system for personalized rehabilitation.
This system integrates a neural network model with a rule-based model to automatically monitor and assess patients' rehabilitation exercises.
Our system can adapt to new participants and achieved 0.81 average performance to assess their exercises, which is comparable to the experts' agreement level.
arXiv Detail & Related papers (2023-05-12T17:37:04Z) - Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy:
Iterative Design and Evaluation with Therapists and Post-Stroke Survivors [66.07833535962762]
Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction.
Previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, but deployment remains a challenge.
We present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient's exercises.
arXiv Detail & Related papers (2021-06-15T22:06:39Z) - Designing Personalized Interaction of a Socially Assistive Robot for
Stroke Rehabilitation Therapy [64.52563354823711]
The research of a socially assistive robot has a potential to augment and assist physical therapy sessions for patients with neurological and musculoskeletal problems.
This paper presents an interactive approach of a socially assistive robot that can dynamically select kinematic features of assessment on individual patient's exercises to predict the quality of motion.
arXiv Detail & Related papers (2020-07-13T16:12:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.