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Android Based AI Powered College Helpdesk Chatbot
Academic institutions face a persistent challenge in delivering timely, accurate, and personalised academic support to large and growing student populations. Conventional Learning Management System (LMS) platforms store considerable volumes of course materials yet lack conversational, context-aware assistance. Existing AI chatbots that rely exclusively on pre-trained Large Language Models (LLMs) frequently produce hallucinated or curriculum-misaligned responses, eroding student trust. This paper presents Student Ease, an Android-based AI powered college helpdesk chatbot that integrates Retrieval-Augmented Generation (RAG) with an LMS backend. The system ingests institutional documents—lecture notes, assignment sheets, and discussion archives—converts them into semantic vector embeddings stored in a FAISS index, and retrieves the most relevant chunks before passing them to an LLM for grounded answer synthesis. A personalization engine tracks each student's query history and learning gaps to provide targeted study recommendations and deadline reminders. System evaluation achieved a Grounded Accuracy of 94.6%, a Citation Precision of 91.8%, a Retrieval Relevance Score of 89.4%, and reduced the hallucination rate from 27% (pure LLM) to 6% (RAG-enhanced). The Flutter/Firebase mobile frontend offers role-based access for students and administrators. Results confirm that combining semantic retrieval with LLM generation substantially outperforms pure LLM baselines in academic helpdesk scenarios.
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