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Possible symptoms of intellectual disability (ID) include delayed physical development that becomes more pronounced as the disability progresses, delayed development of gross and fine motor skills, sensory perception problems, and difficulty grasping the integrity of objects. Although there is no cure or reversal, research has shown that extensive training and learning can lead to easier social integration, but the human demands of diagnosis and the cost of training often result in overburdened families of origin, an unmanageable workload for teachers, and high social costs. Therefore, it is important to conduct efficient, effective, and economical assessments in a safe and reproducible training environment. Currently, the assessment of ID relies on intelligence tests, such as the Wechsler intelligence scale and the vineland adaptive behavior scale. With the rapid development of virtual reality (VR) and machine learning (ML), we created a virtual supermarket and then collected data in three areas, including eye movements, brain waves, and behaviors. We also propose an intelligent executive function evaluation using ML to develop a more objective and automatic evaluation model based on real data through physiological data obtained from user reflections. Statistical analysis of the obtained data showed that some data metrics derived from behavioral information differed significantly between ID patients and healthy participants. This shows that it is possible to perform classification through neural networks, even at multiple levels, which may prove effective for vocational training through VR.