Prediction of Gender-Biased Perceptions of Learners and Teachers Using Machine Learning

Computers have enabled diverse and precise data processing analysis for decades. Researchers of humanities social sciences are increasingly adopting computational tools such as artificial intelligence (AI) machine learning (ML) to analyse human behaviour in society by identifying patterns within data. In this regard, paper presents the modelling teachers students’ perceptions regarding gender bias text books through AI. The was collected from 470 respondents a questionnaire using five different themes. analysed with support vector machines (SVM), decision trees (DT), random forest (RF) neural networks (ANN). experimental results show that prediction varies according theme leads performances AI techniques. However, it is observed when all themes combined, best obtained. ANN, on average, demonstrates performance achieving an accuracy 87.2%, followed RF SVM, which demonstrate 84% 80%, respectively. This significant AI, contribution field.