Formulating SQL Queries from Natural Language Processing for Students Using Mobile Learning

Mobile learning opens new worlds of information and personal development. To enable more personalized learning via mobile devices, several clever approaches should be implemented into mobile-assisted learning systems. This paper examines and explores several systems created with natural language processing (NLP) to extract relevant information from a database by utilizing structured natural language questions as input and SQL queries as output. Natural Language Processing (NLP) tools can be used to evaluate students' flaws throughout the mobile learning assessment process. Furthermore, the approach broadens its use beyond student learning by incorporating CSV data retrieval capabilities. Users, such as placement cell workers dealing with student databases, can perform natural language searches to retrieve useful information from CSV files. The design of the proposed model includes a user interface for submitting English inquiries, followed by NLP modules for analyzing the queries and mapping them to SQL queries. The SQL queries may then be run to obtain the necessary data from the database. It illustrates the power of natural language processing techniques in supporting mobile learning and enhancing data retrieval operations.