Data-Driven Education: Integrating Intelligent Systems with Artificial and Natural Intelligence

Abstract

This article shows the use of natural intelligence and artificial intelligence in education to increase the optimization of student success, get them a quality education, and reduce the workload of teachers. In order to usefully apply natural intelligence and artificial intelligence in teaching, we have developed a Integrity and fuzzy machine learning model of " Integrity Model of a Language " and "Integrity and Fuzzy Model a Language" in education aimed at solving  three fundamental challenges in education: "What should be taught?", "How should it be taught?", and "What knowledge, skills, and critical thinking abilities will students acquire through the learning process?". The proposed solutions involve: 1) restructuring the content of knowledge to answer "What should be taught?", 2) selecting appropriate educational technologies to address "How should it be taught?", and 3) determining the outcomes in terms of what students will gain from the learning process. Students, interacting with logically structured knowledge in a constructive educational process, build their IQ and EQ . The illustrated model of intelligent systems in education was presented with the Russian language as an example. The tools of this model can be applied not only to other languages but also to various natural and humanitarian educational an academic subject domains. This versatility arises from the fact that language serves as the medium for expressing all branches of knowledge. When language encapsulates categorical and invariant knowledge, it enables the simulation of various subject domains within the hybrid fuzzy and integrity-based model of knowledge and rules

Keywords: Intelligent Methodology in Education, Fuzzy Logic & Integrity logic, Machine Learning (ML), Personal learning, Natural Intelligence (NI) & Artificial Intelligence-(AI), Intelligent Information Language Processing