Intelligent Course Recommender Chatbot Using Natural Language Processing

Tan Chia Wei, Mohd Hanafi Ahmad Hijazi, Suraya Alias, Ag Asri Ag Ibrahim, Mohd Fairuz Iskandar Othman


Selecting elective courses in university is challenging for students as they do not know if the courses fit their interests and provide relevant knowledge or skills for their future professions. In some cases, students may register for courses without truly understanding the courses, eventually leading to course selection mistakes. A system that can recommend courses based on student's preferences is deemed necessary to address this problem. This paper proposed an intelligent course recommender system that helps students find suitable courses based on their strengths and interests. It consists of two phases. First, an intelligent course matching engine is designed and developed. The student's input is processed using natural language processing. A convolutional neural network is used to perform Part-of-Speech tagging. Keywords are identified from the processed input, and keyword matching is performed between the student's input and the courses' keywords. The most relevant courses are identified. Second, a chatbot is developed to implement the developed intelligent course matching engine. The chatbot captured student's preferences using a human-like conversation and recommended the identified most relevant courses to the students. The system is evaluated by a group of students in Universiti Malaysia Sabah. The evaluation of the usability and functionality results shows the acceptability of the proposed system, although some future work is needed based on the feedback received.


Recommender system; natural language processing; chatbot

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