Multilevel Modeling of Training Needs in Artificial Intelligence (Q8217)

Nowadays, Artificial Intelligence (AI) is playing a rapidly increasing role in several fields of research and in almost all sectors of real life. However, few studies have assessed the effects of AI applications on training needs. This paper proposes an innovative multilevel modeling in order to investigate Awareness, Attitude and Trust towards AI and their reflections on learning needs. In particular, it is shown how a machine learning variable selection algorithm can support the definition of the optimal subset of all relevant covariates with respect to the outcome variable and improve the multilevel model performance for estimating the probability of educational needs. Thus, starting from a complex web survey to European citizens distributed in eight countries, the estimation of a multilevel binary model defined on the basis of covariates selected through the random forest Boruta algorithm is proposed. A discussion on the gender differences of the related estimated multilevel logit models is presented. A sensitivity analysis is also included in order to assess the prediction accuracy of the proposed multilevel logit modeling. This repository contains data generatedfor the manuscript: " A two-stage procedure for optimal modeling of the probability of training needs in artificial intelligence". It comprehends: (1) the dataset Data_Boruta_Random_Forest used to estimate the variables importance. (2) the dataset Data_Multilevel to perform the comparison among different multilevel binary models proposed in the paper.