AI-BASED EDUCATIONAL ASSESSMENT TECHNIQUES FOR CHILDREN WITH MULTIPLE DISABILITIES: CURRENT PRACTICES, EMERGING TRENDS, AND FUTURE DIRECTIONS

Abstract

The primary objective of this research was to investigate AI-based instructional assessment for children with multiple disabilities, focusing particularly on current methods, developing trends and future possibilities. The research method was a quantitative descriptive survey approach through which data were collected from 350 special education teachers working in special and inclusive educational settings. A self-prepared questionnaire containing demographic variables and 40 structured items was the instrument for data collection. The tool was reviewed by experts and showed excellent reliability. Results showed that teachers thought positively about the benefits and future capabilities of AI educational assessment, however current use was at a moderate level. The study also revealed that training, academic qualification, and teaching experience were bases for significant differences, while limited differences appeared for some other demographic variables. AI-based assessment can facilitate individual, inclusive, and efficient evaluation for children with multiple disabilities, the study stated. It advised teacher training, the creation of AI instruments that are specialized, and a robust policy framework as a way forward to implementation effectiveness.

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