Author(s)

JiaXin Wang^1#, ZiHan Qi^1#, JingJing Li¹, XuanEr Chen², Yuan Lin^1*

Affiliation(s)

¹Business School, Dalian University of Technology, Panjin 124221, Liaoning, China.

²School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, Liaoning, China.

Corresponding Author

Yuan Lin

ABSTRACT

This study addresses the pain points in university admissions consultation, such as "human response bottlenecks" and "information overload and fragmentation", by proposing an intelligent admissions consultation system solution based on Retrieval-Augmented Generation (RAG) technology. The system adopts the LangChain framework, integrates Large Language Models (LLMs) with RAG technology, and constructs a complete workflow including knowledge base construction, retrieval enhancement, and answer generation. Experiments show that the system significantly outperforms the control groups in key metrics, including overall accuracy (95.6%), security compliance rate (96%), and dynamic personality matching rate (92.3%). This system not only effectively improves the response efficiency and answer quality of admissions consultation but also provides a replicable and scalable practical reference for the digital and intelligent upgrading of university admissions management services.

KEYWORDS

Retrieval-Augmented Generation; Large language model; Intelligent question-answering system; University admissions counseling

CITE THIS PAPER

JiaXin Wang, ZiHan Qi, JingJing Li, XuanEr Chen, Yuan Lin. The construction, evaluation, and application of an intelligent admissions Q&A system based on Retrieval-Augmented Generation (RAG). World Journal of Information Technology. 2026, 4(1): 53-63. DOI: https://doi.org/10.61784/wjit3083.

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