Enhancing Personalised Learning with a Context-Aware Intelligent Question-Answering System and Automated Frequently Asked Question Generation

The increasing integration of Artificial Intelligence (AI) in education has led to the development of innovative tools like Intelligent Question-Answering Systems (IQASs), aiming to revolutionize traditional learning paradigms. However, many existing IQAS struggle with the nuances of natural language and the complexities of student questions. This research focuses on developing a context-aware IQAS that leverages advanced Natural Language Processing (NLP) techniques and contextual information, including student learning history and educational content, to provide personalised support. This study also introduces a software tool that utilizes NLP techniques to automatically generate FAQs from educational materials. Employing a hybrid approach combining rule-based and machine learning techniques, the IQAS demonstrated high accuracy in interpreting and responding to a wide range of student queries. The software tool effectively automated the generation of FAQs, creating a valuable resource for personalised learning. The findings suggest that these tools can significantly improve student engagement, motivation, and learning outcomes, highlighting the potential of AI to transform education and pave the way for more personalised, adaptive, and effective learning environments.