Providing an educational eva‎luation model using causal artificial intelligence

1/1/1900 12:00:00 AM

The ever-increasing role of artificial intelligence and the various tools it provides has caused the influence of this part of science to increase in various subjects, including education. One of the points of application of this tool is the eva‎luation of education; By explaining what factors are involved in student eva‎luation and have a direct (causal) effect on it. With this introduction, this article examines the influencing variables on students' academic eva‎luation using the causal learning framework. The purpose of this research is to identify causal relationships between students' academic eva‎luation and targeted key variables, i.e., gender, field of study, and place of study using a precise causal methodology to investigate using a data-set including academic performance indicators, demographic information, and eva‎luation scores. whether these variables independently affect the academic eva‎luation process or not. One of the causal inference techniques, causal meta-learning, is used to determine causal effects. This research seeks to provide nuanced insights into the specific effects of gender, field of study, and geographic location on students' academic eva‎luation, which contributes to a deeper understanding of the multifaceted factors that shape educational outcomes. The findings of this study have shown the impact of these variables on the results of students' eva‎luation, which indicates the relative existence of justice in this field among different student populations. The eva‎luation of the used models has been done relatively by the curves of Gain and Qini and confidence intervals.

استنتاج علّي , هوش مصنوعي علّي , يادگيري ماشين علّي , آموزش , تعليم و تربيت

causal inference , causal artificial intelligence , causal machine learning , education , training

Mohammad Hossein Valikhani

Mohammad Reza Rasouli