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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Students with special needs often require more assistance and attention to meet their educational needs. However, schools frequently grapple with a critical shortage of special education teachers and support staff. This shortage of special education teachers can result in limited resources for general and subject teachers (e.g., math, science), making it challenging to provide individualized support to students with special needs. Specifically, subject teachers may struggle to design effective curricular content modifications and accommodations for such students without the guidance and suggestions of special education teachers. Artificial Intelligence (AI) technologies can provide some support for teachers and schools in meeting the needs of students with special needs. Also, AI may help reduce teachers’ workload. In this technology review, we assess the capabilities of Magic School AI (MSAI) in providing accommodations and modifications to assist teachers in streamlining their workload and fostering inclusivity in their classrooms. We examined five functions: text leveler, text scaffolders, assignment scaffolder, exemplar and non-examples, and sentence starters. Additionally, we discuss the limitations of MSAI and conclude by suggesting potential improvements for the system.

Details

Title
Technology Review of Magic School AI: An Intelligent Way for Education Inclusivity and Teacher Workload Reduction
Author
Li Xiaying 1 ; Li, Belle 2 ; Li, Jianing 1 ; Su-Je, Cho 1 

 Division of Curriculum and Teaching, Fordham University, New York, NY 10023, USA 
 Department of Curriculum and Instruction, Purdue University, West Lafayette, IN 47907, USA 
First page
963
Publication year
2025
Publication date
2025
Publisher
MDPI AG
e-ISSN
22277102
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3244010477
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.