Empowering the Evaluation of Abilities of Children with Autism Spectrum Disorder through Artificial Intelligence: Core Issues and Practical Frameworks——Take imitation ability as an example

ZHANG Ru-jing; ZHOU Ping

doi:10.3969/j.issn.1672-4933.2026.02.005

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Empowering the Evaluation of Abilities of Children with Autism Spectrum Disorder through Artificial Intelligence: Core Issues and Practical Frameworks——Take imitation ability as an example

Artificial Intelligence Rehabilitation Education | 更新时间：2026-07-30

- Empowering the Evaluation of Abilities of Children with Autism Spectrum Disorder through Artificial Intelligence: Core Issues and Practical Frameworks——Take imitation ability as an example
- Chinese Scientific Journal of Hearing and Speech Rehabilitation Vol. 24, Issue 2, Pages: 133-137(2026)
- 作者机构：
  
  聊城大学传媒技术学院山东 252059
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1672-4933.2026.02.005
  CLC： R494
- Received：07 November 2025，
  
  Online First：17 March 2026，
  
  Published：15 March 2026
- 稿件说明：
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张如静,周苹.人工智能赋能孤独症谱系障碍儿童能力评价：核心问题与实践框架[J].中国听力语言康复科学杂志,2026,24(02):133-137.

ZHANG Ru-jing,ZHOU Ping.Empowering the Evaluation of Abilities of Children with Autism Spectrum Disorder through Artificial Intelligence: Core Issues and Practical Frameworks[J].Chinese Scientific Journal of Hearing and Speech Rehabilitation,2026,24(02):133-137.
张如静,周苹.人工智能赋能孤独症谱系障碍儿童能力评价：核心问题与实践框架[J].中国听力语言康复科学杂志,2026,24(02):133-137. DOI： 10.3969/j.issn.1672-4933.2026.02.005.

ZHANG Ru-jing,ZHOU Ping.Empowering the Evaluation of Abilities of Children with Autism Spectrum Disorder through Artificial Intelligence: Core Issues and Practical Frameworks[J].Chinese Scientific Journal of Hearing and Speech Rehabilitation,2026,24(02):133-137. DOI： 10.3969/j.issn.1672-4933.2026.02.005.

摘要

人工智能赋能的能力评价属于能力水平预测模型，忽略了数据特征与能力水平的映射关系。本研究从能力评价研究现状出发，以能力水平可辨识、能力异常可归因和干预决策可理解为目标，提出人工智能赋能孤独症谱系障碍（autism spectrum disorder， ASD）儿童能力评价需解决明确测量范畴，解释能力水平和理解决策结果3个核心问题，并以能力建模-精细评价-可视化反馈提出实践框架。本研究以ASD儿童模仿能力为例开展初步实验，为模仿能力干预精准化提供客观依据，以期为人工智能赋能ASD儿童能力评价提供新思路，为人工智能赋能能力评价技术探索提供参考。

Abstract

The evaluation empowered by artificial intelligence (AI) currently yields a predictive model for capability levels

while the mapping relationship between data features and capability levels was ignored. Firstly

this study summarized the related research on capability evaluation. Secondly

three core issues were proposed: clear measurement categories

interpretable capability levels

and understandable decision results aimed at identifying capability levels could be identified

capability anomalies could be attributed

and how intervention decisions were formulated could be understood. Correspondingly

a practical framework for AI-empowered capability evaluation was proposed through "capability modeling -- fine evaluation -- visual feedback". Based on this

a preliminary experiment was conducted using the imitation ability of children with autism spectrum disorder(ASD) as an example. Lastly

this evaluation method could achieve refinement of imitation ability evaluation and provide an objective basis for precision intervention. This study proposed new ideas for how AI-empowered capability is evaluated

providing useful references for further technological exploration of AI-empowered capability evaluation.

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references

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