Abstract
With the development of mobile devices and the activation of messenger use, it is necessary to use chatbot as a communication tool with students. If individuals can ask various questions and receive customized answers owing to differences in levels among students, it will help improve academic achievement. For students to naturally communicate with chatbot, they must be able to provide familiar and accurate answers similar to those of Georgia Tech's Jill Watson. In this study, a chatbot capable of natural language processing and improving the Seq2Seq algorithm of the long short-term memory structure was implemented. The implemented chatbot learned from the open daily life conversation data, and consequently, it exhibited high accuracy and low loss rate within a short time, and the answer was excellent.
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