Artificial Intelligence-Based Quality Management and Detection System for Personalized Learning

Abstract

Recently, the teaching and learning method in the conventional engineering education system needs a group of learners with personalized learning paths. The introduction of technologies like Artificial Intelligence will aid the learners to identify and detect learning opportunities utilizing historical information, present student profile and success data from an institution, and recommend learning measures to enhance their performance. This study proposes an Artificial Intelligence-based Meta-Heuristic Approach (AIMHA) for personalized learning detection systems and quality management. The proposed model has been utilized to optimize learning effectiveness by considering the nature of the learning path and the number of simultaneous visits to every learning action. In addition, a quality resolution can be determined by a meta-heuristic approach. The simulation findings of the learning actions have been utilized to examine the efficiency of the suggested method. The proposed method is evaluated learning activities achieved an efficiency ratio of 92.3%, sensitivity analysis ratio of 88.4%, performance ratio of 92.3%, precision ratio of 94.3% compared to other existing models.