Gamification and XAI Tutoring Structure using Mamdani Fuzzy Inference Systems

The teaching methodology is important for the current student learning system because slow-learning and fast-learning students are studying. So, the problem with improving the teaching methodology and teaching style changes is needed. Motivate and engage the poor or slow-learning students do not compare with slow-learning and fastest-learning students because some students may already have genetically quick learning. The new proposed learning method is called Gamification and Explainable Artificial Intelligence (XAI) Tutoring Structure, and Mamdani Fuzzy Inference Systems do the more engaging and improve the personalized learning. Using this technology, educators can gather and examine information on test results, time spent on learning activities, involvement in discussion forums, and more. Collect the input data using the student dataset and pre-processing using aggregation methods to collect students' information from the student dataset. A support vector machine (SVM) is used to filter the dataset and find the matching student data, student learning analysis classification using Artificial Intelligence (XAI) Tutoring Structure and Mamdani Fuzzy Inference Systems. The classification result is student engagement and educator instructions. This data analysis aids educators in identifying high achievers and pupils who struggle. The efficacy of this technique was successfully deployed in a trial project involving 1000 computer science students. By fusing quantitative information with qualitative insights, this study offers a thorough knowledge of the effects of gamification.