Assayed, Suha KhalilAlkhatib, ManarShaalan, Khaled2024-04-052024-04-052024Assayed, S.K., Alkhatib, M., Shaalan, K. (2024). Enhancing Student Services: Machine Learning Chatbot Intent Recognition for High School Inquiries. In: Al Marri, K., Mir, F.A., David, S.A., Al-Emran, M. (eds) BUiD Doctoral Research Conference 2023. Lecture Notes in Civil Engineering, vol 473. Springer, Cham. https://doi.org/10.1007/978-3-031-56121-4_24Print: 978-3031561207 Online: 978-3031561214https://bspace.buid.ac.ae/handle/1234/2568https://link.springer.com/chapter/10.1007/978-3-031-56121-4_24This open access book presents contributions on a wide range of scientific areas originating from the BUiD Doctoral Research Conference (BDRC 2023)Purpose - This paper aims to develop a novel chatbot to improve student services in high school by transferring students’ enquiries to a particular agent, based on the enquiry type. In accordance to that, comparison between machine learning and neural network is conducted in order to identify the most accurate model to classify students’ requests. Methodology - In this study we selected the data from high school students, since high school is one of the most essential stages in students’ lives, as in this stage, students have the option to select their academic streams and advanced courses that can shape their careers according to their passions and interests. A new corpus is created with (1004) enquiries. The data is annotated manually based on the type of request. The label high-school-courses is assigned to the requests that are related to elective courses and standardized tests during high school. On the other hand, the label majors & universities is assigned to the questions that are related to applying to universities along with selecting the majors. Two novel classifier chatbots are developed and evaluated, where the first chatbot is developed by using a Naive Bayes Machine Learning Algorithm, while the other is developed by using Recurrent Neural Networks (RNN)-LSTM. Findings - Some features and techniques are used in both models in order to improve the performance. However, both models have conveyed a high accuracy score which exceeds (91%). The models have been validated as a pilot testing by using high school students as well as experts in education and six questions and enquiries are presented to the chatbots for the evaluation. Implications and future work - This study can add value to the team of researchers and developers to integrate such classifiers into different applications. As a result, this improves the users’ services, in particular, those implemented in educational institutions. In the future, it is certain that intent recognition will be developed with the addition of a voice recognition feature which can successfully integrated into smartphones.enintent recognition, LSTM, Naive-Bayes, chatbot, high school, machine learningConference paper