Ai-Driven Adaptive It Training: A Personalized Learning Framework For Enhanced Knowledge Retention And Engagement

Abstract

In traditional training designs, insufficient attention is paid to build an efficient knowledge management process because of usual generic training approaches, which leads to the lack of performer interest and engagement, repetitive learning, and minimal knowledge retention. This paper discusses about an intelligent IT training model which has been developed to automate the process of selecting appropriate material, increasing learner interest, and improving the effectiveness of training. Here it is important to note that the system uses AI, ML, NLP, and Cloud Computing to make changes to the content depending on the learning outcomes, statistics, and comments from the learners. The main components of the proposed system are the data acquisition sub-module, the AI core engine, and the learning improvement sub-module to achieve real-time training. The effectiveness of the system was, therefore, tested under controlled experiments with 200 IT professionals in which the product of adaptive learning based on artificial intelligence was compared with traditional models of training that employ static approaches. The outcome as per the tests showed that Interactive training through the AI system improved knowledge retention by 35% and increased the level of engagement by 40% and reduced the training time among the user by 22%. Moreover, the model aided by artificial intelligence was completed at a 94% level and it is higher compared to traditional methods.