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Automated learning methodologies employing intelligent systems, have become increasingly popular in the internet due to advancements in the field of machine learning. In our paper, we examine the case of intelligent language tutoring system (ILTS), which has helped increase productivity and reduce overhead costs by automating teaching processes. However, current ILTSs are restrictive when it comes to integrating functionalities such as context-based understanding, and allowing text input from user. This paper proposes a framework, incorporated into an intelligent language tutoring system, based on a string search algorithm that extracts only vital word patterns from a pre-defined 'items bank'. This ensures that only required Basic English patterns are acquired, thereby facilitating the system to deliver accurate context based results and handle advanced structures with ease. Comprehensive evaluation of the system's performance indicates the system has proven to be efficient and robust structure, providing favorable alternatives to Natural Language Processing (NLP) systems.