The use of Artificial Intelligence for Intervention and Assessment in Individuals with ASD
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909601503379456 |
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| author | Sideraki, Aggeliki Anagnostopoulos, Christos-Nikolaos |
| author_facet | Sideraki, Aggeliki Anagnostopoulos, Christos-Nikolaos |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_02747 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The use of Artificial Intelligence for Intervention and Assessment in Individuals with ASD Sideraki, Aggeliki Anagnostopoulos, Christos-Nikolaos Artificial Intelligence Computers and Society 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. |
| title | The use of Artificial Intelligence for Intervention and Assessment in Individuals with ASD |
| topic | Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2505.02747 |