Towards Developing a Multimodal Chat Assistant for University Stakeholders: RAG-based Approach
arXiv:2607.01115v1 Announce Type: new Abstract: University stakeholders often face difficulties in accessing timely and reliable information, especially in developing countries, where there are very few intelligent support systems. Existing rule-based chatbots are unable to handle complex, domain-specific queries and are not well-equipped to adapt to evolving institutional policies. As a fill-in-the-gap solution, we present the multimodal university chatbot with retrieval-augmented generation. The system combines the large language model with semantic retrieval to produce context-based respons
The proliferation of more capable large language models and advancements in embedding techniques make it feasible to develop RAG-based systems for specific, complex domains like university information.
This development indicates a growing trend towards specialized AI applications that can autonomously handle complex informational needs, particularly in institutions and sectors currently underserved by intelligent systems.
Traditional rule-based chatbots are becoming obsolete for domain-specific queries, with multimodal RAG-based solutions offering more dynamic and context-aware information access for stakeholders.
- · AI agents developers
- · Educational institutions in developing countries
- · Open-source LLM communities
- · Students and faculty
- · Legacy rule-based chatbot vendors
- · Information desk services
- · Manual data retrieval processes
Universities gain more efficient and personalized information dissemination for their stakeholders.
The precedent set by RAG-based systems in universities could accelerate their adoption in other complex institutional settings.
This could contribute to reducing information asymmetries and improving administrative efficiency across various public and private sectors globally.
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