AI Personalizing Training and Reskilling Employees for the Digital Age

Organizations are investing in reskilling and individualized training programmers to stay up with the digital era's fast-paced technical progress. This paper provides a novel strategy that leverages AI principles to make these algorithms even more effective. We focus on two important components: decision tree imputation preprocessing and better generative adversarial neural network (GAN) classification/prediction. During the preprocessing stage, Decision Tree Imputation is utilised to fill in missing data in training datasets. When confronted with little data, it is usual for conventional methodologies to offer biassed results and less-than-ideal models. Nonetheless, Decision Tree Imputation appears as a viable choice due to its creative utilisation of pre-existing data to fill in dataset gaps. This ensures a more thorough and representative training dataset, which results in more accurate AI models. For individualised training recommendations, we propose utilising an Improved Generative Adversarial Neural Network (GAN), which goes beyond traditional classification and prediction models. Enhanced GANs, which are well-known for their ability to generate synthetic data, are used to create personalised learning pathways for each employee. The improved GAN takes into account the user's current skill set, as well as their learning style, future work aspirations, and the ever-changing needs of the digital world. This research proposes an integrated system that can handle bad data and personalise training recommendations for each employee by using powerful AI models and preprocessing approaches.