Appl. Math. Inf. Sci. 18, No. 3, 629-640 (2024) 629 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.18576/amis/180315 Developing an Educational Chatbot for Scientific Data Management Course Using DialogFlow Manal M. A. Abdelmoiz 1,∗ , Mohamed M. M. Mostafa 2 and Taysir H. A. Soliman 1 1 Information System Department, Faculty of Computers and Information, Assiut University, Assiut, Egypt 2 Department of Foundations of Education, Faculty of Education, Assiut University, Assiut, Egypt Received: 3 May 2023, Revised: 2 Feb. 2024, Accepted: 7 Feb. 2024 Published online: 1 May 2024 Abstract: The COVID-19 pandemic has radically altered the worldwide learning environments, setting the stage for Electronic Learning (E-Learning) advancement, where remote learning is facilitated through digital tools. The key stakeholders (professors, teaching assistants, and students) face bottlenecks as they shift to online education providing blended learning. New learning tools based on Natural Language Processing (NLP) are provided. Designing a chatbot is one of the solutions to deal with this issue. Chatbots are simple computer programs that attempt to simulate human conversation using Artificial Intelligence (AI) and NLP. They allow learners to have a standardized learning environment. In this paper, we have set up a chatbot application named Scientific Data Management BOT (SDMBOT) for handling E-Learning activities specifically for Scientific Data Management (SDM) courses based on AI and NLP techniques using Dialogflow Framework, a Google development platform for building NLP-based human-computer interface solutions. SDMBOT was trained on a dataset that was specifically created based on course content. The web or mobile app, via which our built chatbot is available, is used for student interaction. Students can ask the chatbot questions concerning SDM, and the chatbot will process the message and respond to the user by displaying the proper result. The accuracy of the SDM chatbot, which is calculated by using a confusion matrix indicated that our chatbot is 74 % accurate. Keywords: Artificial Intelligence, COVID-19, Data Management, Educational Chatbot, E-Learning, Machine Learning, NLP. 1 Introduction Technology’s rapid growth has created many new solutions and approaches to meet all students’ objectives based on their educational goals and demands. Today’s students may take notes on a tablet, capture images or add screenshots of the lecturer’s presentation to his notes, send the professor an email at any time, and verify any required tasks at a learning management platform. The ability to communicate with the instructor outside of the classroom becomes less effective as the number of students rises, even though these technologies benefit the students by enhancing their understanding of the lecturer’s response to a particular question. Also, professors may not always reply to a student right away due to their busy schedules. A difficulty occurs when a lecturer might not be accessible or unable to respond to specific queries when necessary. Furthermore, the COVID-19 pandemic has radically altered the worldwide learning environments, providing the foundations for developing E-Learning, where teaching on digital platforms is carried out remotely. One of the latest technologies that meet this need is chatbots. Software programs, known as chatbots or ”conversational agents”, imitate spoken or written human language to simulate a conversation or other contact with a real person. NLP is the innovation at the heart of the chatbot’s rise. NLP can recognize text and spoken words just like humans, translate text from one language to another, answer voice commands, and quickly encapsulate huge amounts of text [1, 2]. The use of chatbots in E-Learning can be viewed as a significant innovation. They are proving to be the most creative way to combine technology with education. Chatbots have the advantage of being available 24/7 to resolve user queries. They also can handle multiple requests without compromising the quality of interactions. The purpose of this study is to develop a chatbot application named SDMBOT using AI and NLP, which takes a text question from a student, extracts intent ∗ Corresponding author e-mail: manalabdelmoiz@aun.edu.eg © 2024 NSP Natural Sciences Publishing Cor.