Adaptive Artificial Intelligent Technique to Improve Acquisition of Knowledge in the Educational Environment

Abstract

It is now possible to make the logical thinking of humans, computer-controlled robots, and technology on the educational Environment platform by using artificial intelligence. These method-based robotic systems begin replacing human resources in all industries, which helps resolve various characteristic difficulties that include emotional skills, creative thinking, cognitive, and depletion in the educational platform. The Adaptive Artificial Intelligence Technique (AAIT) has been refined in this study to fulfill the learning objective of increased higher-order learning and skill acquisition in the Educational Environment. It enables more intelligent decision-making by taking into account all essential aspects of a company. As a result, big data plays a crucial role in today’s businesses. A powerful tool for proactive and automated management when combined with artificial intelligence (AI). Saving time and money by automating and optimizing routine tasks may be possible for your business with the right AI technology. Productivity and operational efficiencies will be increased. Use cognitive technologies to make faster business decisions. Therefore, the learning achievement model (LAM) and Risk Student Model (RSM) are established on the educational environment platform. As well as optimizing interpersonal resilience, the Learning Achievement Model is also utilized to increase innovative abilities in the Educational Platform. In the Danger Class Diagram, the Estimation Algorithm reduces the risk of learning failure, enhances cognition, and deprives the recognizing platform. Experiments and simulations are conducted to determine the reliability of the proposed framework based on the accuracy, F-Means, Sensitivity, specificity, and Performance (SSP).