ARTIFICIAL INTELLIGENCE INNOVATION IN EDUCATION

The focus of this research would be to see how Artificial Intelligence (AI) has changed education.
Education today is essential to an individual's and society's progress. The purpose of the study was to uncover and develop the usage of Artificial Intelligence (AI) in the educational sphere, as well as to come up with previously unknown applications. Artificial Intelligence investigates the fresh talent for educational technology innovation. Artificial intelligence has evolved into a critical element of today's education sector and a critical technique for gaining market competitive advantages. The uses of artificial intelligence in education are discussed in this paper, as well as existing tools and applications, market and research trends, opportunities, risks, and present limitations of AI in education.
Based on multiple intelligence framework analysis and machine learning, this paper integrates multiple intelligence theory and artificial intelligence technology, notices the teaching situation through smart voice and image interaction, and provides smart teaching auxiliary services for teachers and students. This study provides a smart and innovative education paradigm and, based on that model, creates a smart teaching assistant system. We concentrate on artificial intelligence-assisted innovation education with multidisciplinary integration as the objective, so that teachers and students may interact more effectively than before. The study's findings efficiently achieve tailored instruction, enjoyable learning, and the growth of unique talents with varied abilities. We examined several works in relevant fields and sub-domains such as big data in education, educational data mining (EDM), and learning analytics for our in-depth review study. Educational applications are examined from several angles in this paper. On one side, a detailed description of the platforms and tools produced as a result of the study is provided. On the other hand, it recognizes the limitations, prospective obstacles, and areas for future improvement, and this serves as a foundation for future e-learning research.