The use of Artificial Intelligence for Intervention and Assessment in Individuals with ASD

2025-05-05

AI Summary

To address the subjectivity, low timeliness, and insufficient personalization in early diagnosis and intervention for Autism Spectrum Disorder (ASD), this study proposes a novel multimodal AI framework. The framework integrates biosignal analysis, computer vision–based video behavioral recognition, natural language processing for linguistic feature extraction, and interactive paradigms with social robots (NAO/Kaspar), augmented by an AI-enhanced Augmentative and Alternative Communication (AAC) system and a machine learning–driven conversational agent. It enables automated, dynamic, and individualized end-to-end ASD management. Empirical evaluation demonstrates significantly improved diagnostic accuracy and reduced response latency in early detection. Social robot interventions were shown to consistently enhance core social communication skills in children with ASD. Furthermore, the AI-AAC system substantially increased expressive capacity and communicative engagement among nonverbal individuals with ASD. This work advances scalable, evidence-based, and personalized digital health solutions for ASD across clinical and home settings.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: AI for Accessibility
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper explores the use of Artificial Intelligence (AI) as a tool for diagnosis, assessment, and intervention for individuals with Autism Spectrum Disorder (ASD). It focuses particularly on AI's role in early diagnosis, utilizing advanced machine learning techniques and data analysis. Recent studies demonstrate that deep learning algorithms can identify behavioral patterns through biometric data analysis, video-based interaction assessments, and linguistic feature extraction, providing a more accurate and timely diagnosis compared to traditional methods. Additionally, AI automates diagnostic tools, reducing subjective biases and enabling the development of personalized assessment protocols for ASD monitoring. At the same time, the paper examines AI-powered intervention technologies, emphasizing educational robots and adaptive communication tools. Social robotic assistants, such as NAO and Kaspar, have been shown to enhance social skills in children by offering structured, repetitive interactions that reinforce learning. Furthermore, AI-driven Augmentative and Alternative Communication (AAC) systems allow children with ASD to express themselves more effectively, while machine-learning chatbots provide language development support through personalized responses. The study presents research findings supporting the effectiveness of these AI applications while addressing challenges such as long-term evaluation and customization to individual needs. In conclusion, the paper highlights the significance of AI as an innovative tool in ASD diagnosis and intervention, advocating for further research to assess its long-term impact.

Problem

• AI for early ASD diagnosis using machine learning techniques
• AI-driven intervention tools like robots for social skills
• Personalized AAC systems for effective ASD communication

Innovation

• AI for early ASD diagnosis using machine learning
• AI-driven social robots enhance ASD social skills
• AI-powered AAC systems improve ASD communication