Shining Child Ltd. (CS)
Apricot Medical Technology Ltd. (CS)
SEN students are common in Hong Kong nowadays. To identify a student with specific learning difficulties (SpLD), usually a screening and a follow-on assessment process is required. However, conventional screening test is a long process which has to be conducted manually by therapists. Due to the shortage of therapists, many SEN students cannot receive the screening in time and hence cannot be identified for early intervention. In this project, an AI-assisted screening approach will be developed to offload the therapists and shorten the evaluation time. Students identified with high LD risk can then receive the intervention or full assessment as early as possible. To do so, we will split the whole screening process into pre-screening and screening phases. In each phase, we will apply computer vision, eye tracking and audio analytic technologies to identify the learning difficulties and analyze the reading/writing performance of the student. Since pre-screening can be conducted in short time, all students in a school can receive the pre-screening test to find out the potential students with LD. After that, more high-risk students can receive the screening test and be identified for early intervention. Moreover, therapists with less clinical experience can assess students’ performance faster and more easily with AI recommendations.
