Nguyen Nang Hung Van;Phuc Hao Do;Van Nam Hoang;Truc Thi Kim Nguyen;Minh Tuan Pham
{"title":"基于人工智能的大学入学咨询:大型语言模型在学生指导中的用例","authors":"Nguyen Nang Hung Van;Phuc Hao Do;Van Nam Hoang;Truc Thi Kim Nguyen;Minh Tuan Pham","doi":"10.1109/TLT.2025.3604096","DOIUrl":null,"url":null,"abstract":"This study investigates how technical advances in large language models (LLMs) translate into measurable educational benefit. University admission counseling plays a crucial role in helping prospective students make their higher education decisions. However, traditional advisory methods are constrained by issues, such as limited scalability, personalization, and the ability to handle large volumes of inquiries. With the growing need for real-time assistance, artificial intelligence (AI), particularly LLMs), presents a promising solution to these challenges. This article introduces an AI-driven university admission counseling system that automates routine inquiries, personalizes guidance, and improves accessibility. We develop a formal mathematical framework to represent the counseling task, using embedded and similarity metrics to assess the compatibility of student profiles with academic programs. The system incorporates a multistage workflow for efficient data processing, embedded generation, and AI-driven recommendation. We evaluated the performance of several LLMs, namely, eLLAMA, eGPT, and eDEEPSEEK, through retrieval-augmented generation, measuring output quality with natural language processing metrics, such as bilingual evaluation understudy, recall-oriented understudy for gisting evaluation, METEOR, and BERTScore. Our results demonstrate that LLMs can significantly improve the efficiency and quality of admission counseling, providing a scalable and adaptable solution that demonstrably enhances student confidence and decision quality.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"856-868"},"PeriodicalIF":4.9000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI-Powered University Admission Counseling: A Use Case of Large Language Models in Student Guidance\",\"authors\":\"Nguyen Nang Hung Van;Phuc Hao Do;Van Nam Hoang;Truc Thi Kim Nguyen;Minh Tuan Pham\",\"doi\":\"10.1109/TLT.2025.3604096\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study investigates how technical advances in large language models (LLMs) translate into measurable educational benefit. University admission counseling plays a crucial role in helping prospective students make their higher education decisions. However, traditional advisory methods are constrained by issues, such as limited scalability, personalization, and the ability to handle large volumes of inquiries. With the growing need for real-time assistance, artificial intelligence (AI), particularly LLMs), presents a promising solution to these challenges. This article introduces an AI-driven university admission counseling system that automates routine inquiries, personalizes guidance, and improves accessibility. We develop a formal mathematical framework to represent the counseling task, using embedded and similarity metrics to assess the compatibility of student profiles with academic programs. The system incorporates a multistage workflow for efficient data processing, embedded generation, and AI-driven recommendation. We evaluated the performance of several LLMs, namely, eLLAMA, eGPT, and eDEEPSEEK, through retrieval-augmented generation, measuring output quality with natural language processing metrics, such as bilingual evaluation understudy, recall-oriented understudy for gisting evaluation, METEOR, and BERTScore. Our results demonstrate that LLMs can significantly improve the efficiency and quality of admission counseling, providing a scalable and adaptable solution that demonstrably enhances student confidence and decision quality.\",\"PeriodicalId\":49191,\"journal\":{\"name\":\"IEEE Transactions on Learning Technologies\",\"volume\":\"18 \",\"pages\":\"856-868\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2025-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Learning Technologies\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11144756/\",\"RegionNum\":3,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Learning Technologies","FirstCategoryId":"95","ListUrlMain":"https://ieeexplore.ieee.org/document/11144756/","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
AI-Powered University Admission Counseling: A Use Case of Large Language Models in Student Guidance
This study investigates how technical advances in large language models (LLMs) translate into measurable educational benefit. University admission counseling plays a crucial role in helping prospective students make their higher education decisions. However, traditional advisory methods are constrained by issues, such as limited scalability, personalization, and the ability to handle large volumes of inquiries. With the growing need for real-time assistance, artificial intelligence (AI), particularly LLMs), presents a promising solution to these challenges. This article introduces an AI-driven university admission counseling system that automates routine inquiries, personalizes guidance, and improves accessibility. We develop a formal mathematical framework to represent the counseling task, using embedded and similarity metrics to assess the compatibility of student profiles with academic programs. The system incorporates a multistage workflow for efficient data processing, embedded generation, and AI-driven recommendation. We evaluated the performance of several LLMs, namely, eLLAMA, eGPT, and eDEEPSEEK, through retrieval-augmented generation, measuring output quality with natural language processing metrics, such as bilingual evaluation understudy, recall-oriented understudy for gisting evaluation, METEOR, and BERTScore. Our results demonstrate that LLMs can significantly improve the efficiency and quality of admission counseling, providing a scalable and adaptable solution that demonstrably enhances student confidence and decision quality.
期刊介绍:
The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.