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

IF 4.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Nguyen Nang Hung Van;Phuc Hao Do;Van Nam Hoang;Truc Thi Kim Nguyen;Minh Tuan Pham
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引用次数: 0

摘要

本研究探讨了大型语言模型(llm)的技术进步如何转化为可衡量的教育效益。大学入学咨询在帮助未来的学生做出高等教育决定方面起着至关重要的作用。然而,传统的咨询方法受到一些问题的限制,例如有限的可伸缩性、个性化和处理大量查询的能力。随着对实时辅助的需求不断增长,人工智能(AI),特别是法学硕士(llm),为这些挑战提供了一个有希望的解决方案。本文介绍了一个人工智能驱动的大学入学咨询系统,该系统可以自动进行日常查询,个性化指导,并提高可访问性。我们开发了一个正式的数学框架来表示咨询任务,使用嵌入式和相似性度量来评估学生档案与学术课程的兼容性。该系统集成了一个多阶段的工作流程,用于高效的数据处理、嵌入式生成和人工智能驱动的推荐。我们评估了几个llm的性能,即eLLAMA, eGPT和eDEEPSEEK,通过检索增强生成,使用自然语言处理指标测量输出质量,如双语评估替代研究,注册评估的面向回忆的替代研究,METEOR和BERTScore。我们的研究结果表明,法学硕士课程可以显著提高入学咨询的效率和质量,提供可扩展和适应性强的解决方案,显著提高学生的信心和决策质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
IEEE Transactions on Learning Technologies
IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
7.50
自引率
5.40%
发文量
82
审稿时长
>12 weeks
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.
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