Optimizing Engineering Student Guidance: An NLP Approach
Ranjana Agrawal, Divyam Dholwani, Aardra Patil, Vijit Ayush Pandey, Sophiya Ahmad, Swapnaja Magarde · 2025
This research discusses creating an interactive guidance chatbot using natural language processing (NLP), specifically TF-IDF, with TensorFlow. The chatbot is developed on a rich dataset of topics in engineering as well as on queries by the students and obtained an impressive 98.63 % accuracy rate in generating personal, context-sensitive responses. The chatbot shows much promise in supporting student queries by identifying them, being able to offer precise guidance to the students, and thereby offloading faculty workload. Such a chatbot fosters student engagement and active learning, thus putting into evidence the transformative aspect of AI for education. The paper reflects possibilities for further research in the future: advanced NLP, multimodal interactions, and integration with an educational platform. In all, this is a first step towards creating tools driven by AI toward personalized learning experiences, a preview of what could come in time to mean efficient and student-focused educational support.