Abstract
In recent years, in the field of education technology, artificial intelligence tutors have come to be expected to provide individualized educational services to help learners achieve high levels of academic success. To this end, AI tutors need to be able to understand the current status and preferences of a learner and then suggest appropriate learning contents accordingly. However, it is challenging to monitor learner status and preferences continually and to recommend appropriate educational services. In this paper, we propose an individualized AI tutor as an integrated system of three developmental learning networks (DLNs) by extending a deep adaptive resonance theory (Deep ART) network, a neural network capable of incremental learning. Specifically, the learner status DLN is able to easily add new input channels about learner status without disrupting existing classifiers. The learner preference DLN is to categorize learner preferences based on frequency as well as sequence of events. The learner experience DLN is updated to immediately reflect alteration of the educational effectiveness in the current classification. Our AI tutor is currently embedded in a commercialized mobile application for teaching the Korean language to children. Experimental results show that the AI tutor application efficiently helps children learn the Korean language.
Highlights
Educational technology (Edutech) gives learners endless opportunities to learn new things and allows learners to customize their learning, taking into account their abilities and mobility
WORK This paper proposed a novel individualized AI tutor to help a learner achieve a high level of academic success
To consider the current learner status and preferences, we developed the AI tutor as a system integrating three developmental learning networks (DLNs) by extending the Deep ART network
Summary
Educational technology (Edutech) gives learners endless opportunities to learn new things and allows learners to customize their learning, taking into account their abilities and mobility. Kim: Individualized AI Tutor Based on DLNs about the learner status observed during the learning process. We propose an AI tutor that provides individualized education programs to learners considering their current status and preferences. To this end, we develop an integrated system of three developmental learning networks (DLNs): learner status DLN, learner preference DLN, and learner experience DLN based on the Deep ART network [16]. If no node satisfies the resonance condition, a new category node is created, and an associated weight vector is initialized by k x. Template Learning: If the resonance condition is satisfied, the weight vector k wj is updated as follows:. The rectangular area k Rj represented by k w(Jold) expands up to an area that includes both the previous rectangular area and the input k I
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