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An Intelligent Tutoring System (ITS) has been shown to improve students learning outcomes by providing a personalized curriculum that addresses individual needs of every student. However, despite the effectiveness and efficiency that ITS brings to students learning process, most of the studies in ITS research have conducted less effort to design the interface of ITS that promotes students interest in learning, motivation and engagement by making better use of AI features. In this paper, we explore AI-driven design for the interface of ITS describing diagnostic feedback for students problem-solving process and investigate its impacts on their engagement. We propose several interface designs powered by different AI components and empirically evaluate their impacts on student engagement through Santa, an active mobile ITS. Controlled A/B tests conducted on more than 20K students in the wild show that AI-driven interface design improves the factors of engagement by up to 25.13%.
In the field of tutoring systems, investigations have shown that there are many tutoring systems specific to a specific domain that, because of their static architecture, cannot be adapted to other domains. As consequence, often neither methods nor k
In most student dorms in developing countries, a large number of people live in single-function dorm units. The division of the dormitory is too fixed, resulting in the dormitory often lacking functional spaces such as entertainment, sports, meetings
In the context of building an intelligent tutoring system (ITS), which improves student learning outcomes by intervention, we set out to improve prediction of student problem outcome. In essence, we want to predict the outcome of a student answering
The primary purpose of this paper is to provide a design of a blockchain-based system, which produces a verifiable record of achievements. Such a system has a wide range of potential benefits for students, employers and higher education institutions.
Designing novel materials that possess desired properties is a central need across many manufacturing industries. Driven by that industrial need, a variety of algorithms and tools have been developed that combine AI (machine learning and analytics) w