AI in Personalized Learning and Educational Assessment

Abstract

By revolutionizing in creation of personalized learning, Intelligent tutoring systems (ITS) have begun to use Artificial Intelligence to push the educational assessment. In this first part, I research how ITS can be used with the analysis of the Carnegie Learning platform which is an already existing AI based educational tool. As an alternative to this, Carnegie Learning combines cognitive modeling techniques with the cutting edge of adaptive algorithms to offer services of personalization of instruction, real time feedback and targeted remedial tailored for a student’s individual learning gap. Students in this system are continuously being monitored with regards to how sophisticated their interaction is, the information is analyzed, and student knowledge gaps are identified; content is made difficult dynamically to fill the students knowledge gaps. On the empirical studies, this has boosted the level of student engagement, subject mastery and self efficacy, especially in the area of mathematics education. Now with this approach it becomes more convenient because it removes such a strict assessment burden from the teacher and promotes deeper understanding via strong synchronization with the learner profiles. However, the paper points that ITS can be done properly Depending on the mechanisms between biases they might arise and the methodology of ITS must be perfected at many educational places.