Lampos Vasileios, Mintz Joseph, Qu Xiao
Department of Computer Science, University College London, London, UK.
Institute of Education, University College London, London, UK.
NPJ Sci Learn. 2021 Sep 1;6(1):25. doi: 10.1038/s41539-021-00102-x.
Effective inclusive education is key in promoting the long-term outcomes of children with autism spectrum conditions (ASC). However, no concrete consensus exists to guide teacher-student interactions in the classroom. In this work, we explore the potential of artificial intelligence as an approach in autism education to assist teachers in effective practice in developing social and educational outcomes for children with ASC. We form a protocol to systematically capture such interactions, and conduct a statistical analysis to uncover basic patterns in the collected observations, including the longer-term effect of specific teacher communication strategies on student response. In addition, we deploy machine learning techniques to predict student response given the form of communication used by teachers under specific classroom conditions and in relation to specified student attributes. Our analysis, drawn on a sample of 5460 coded interactions between teachers and seven students, sheds light on the varying effectiveness of different communication strategies and demonstrates the potential of this approach in making a contribution to autism education.
有效的全纳教育是促进自闭症谱系障碍（ASC）儿童长期发展成果的关键。然而，目前尚无具体的共识来指导课堂上的师生互动。在这项工作中，我们探索人工智能作为自闭症教育方法的潜力，以帮助教师在为ASC儿童制定社会和教育成果方面进行有效实践。我们制定了一个协议来系统地捕捉这种互动，并进行统计分析，以揭示所收集观察结果中的基本模式，包括特定教师沟通策略对学生反应的长期影响。此外，我们部署机器学习技术，根据教师在特定课堂条件下使用的沟通形式以及与特定学生属性相关的情况来预测学生的反应。我们基于教师与七名学生之间5460次编码互动的样本进行分析，揭示了不同沟通策略的不同有效性，并证明了这种方法对自闭症教育做出贡献的潜力。
