Using Multi-Source Integration and Information Retrieval Technology to Program Question-and-Answering Chatbots

Abstract

Despite rapid growth in the number of programming learners, the continuous influx of new information and a lack of human guidance has left many learners sifting through online resources for reliable content. Moreover, many learners struggle to frame questions that accurately capture their specific concerns. This study addressed the growing need for efficient learning resources by developing a microservice-based Chatbot to synthesize information from diverse sources for programming learners. The proposed MPAbot system utilizes keyword extraction and cross-referencing across multiple sourced posts to help users solve questions more effectively. MPAbot incorporates word embeddings, sentence similarity, Latent Dirichlet Allocation (LDA) topic modeling, and multi-criteria decision analysis to filter out redundant information, thereby reducing browsing time and enhancing learning efficiency. Besides, MPAbot also integrates generative AIto improve the accuracy of its answers to users' questions. Experimental results show that our GPT-ranked approach achieved a 52.43% higher mean rank score compared to purely GPT-generated responses in user evaluation tests, demonstrating the effectiveness of the proposed multi-source integration approach.