Implementation of an Educational Chatbot using Rasa Framework
Supreetha H V1, Sandhya S2
1Supreetha H V, Department of Computer Network Engineering at RV College of Engineering (RVCE) Bengaluru (Karnataka), India.
2Dr. Sandhya S, Assistant Professor, Department of Computer Science and Engineering, RV College of Engineering (RVCE) Bengaluru (Karnataka), India.
Manuscript received on 04 July 2022 | Revised Manuscript received on 17 July 2022 | Manuscript Accepted on 15 August 2022 | Manuscript published on 30 August 2022 | PP: 29-35 | Volume-11 Issue-9, August 2022 | Retrieval Number: 100.1/ijitee.G91890811922 | DOI: 10.35940/ijitee.G9189.0811922
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The growth in Artificial Intelligence (AI), Big-data,and Internet-of-Things (IOT) technologies has increased chabot’s application in many areas. Some of the applications of chatbot can be seen in areas such as social media, e-commerce, healthcare, stock market, education, banking sector etc. Most of the high-end chatbots are deployed inside e-commerce, banking and health websites. There is a need to deploy the chatbots in educational website to improve interactivity of the educational platforms. The main target users of this website is rural students. In rural areas, probability of students dropping school after some age is common because, there won’t be proper monitoring of students and also sometimes facilities will be less. With e- learning, anyone can learn everything with limited cost. The key insight of developing this e-learning website is to provide a chatbot which can motivate rural students towards education. Thus a single platform where users can learn different courses, take quizzes, and chat with the bot is developed. It also provides an additional facility of tracking the scores of the quizzes and giving personalized recommendation systems to improve the scores. The chatbot will also help users to find details aboutfaculties and help users to set an appointment with distant faculties in online mode for doubts clarification. Flask micro- framework is used for developing the website. Firebase is used to store the data. RASA framework is used in developing the chatbot. Finally a content based filtering is used to givepersonalized recommendation systems.
Keywords: Chatbots, Content based learning, Firebase, Natural Language Processing, Recommendation Systems, RASA.
Scope of the Article: Natural Language Processing