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Knowledge-augmented form-filling agent for higher education services.

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Title: Knowledge-augmented form-filling agent for higher education services.
Authors: Sang, Haiwei1 (AUTHOR) haiwei.sang@gmail.com, Liu, Mengyun2 (AUTHOR) lmy4pub@gmail.com, Tang, Jing3 (AUTHOR) tangjing202209@163.com, Yang, Yuxiang4 (AUTHOR) gs.yuxiangyang22@gzu.edu.cn, Chen, Yuling4 (AUTHOR) ylchen3@gzu.edu.cn
Source: International Journal of Educational Technology in Higher Education. 7/6/2026, Vol. 23 Issue 1, p1-20. 20p.
Subject Terms: *Knowledge base, *Universities & colleges, *Information retrieval, Language models, Data extraction, Data entry
Abstract: Online academic and administrative services in higher education increasingly require students, faculty, and staff to complete structured forms across heterogeneous institutional platforms. These tasks are complicated by variations in field naming, page layout, validation rules, and dynamic interaction logic, which limits the reliability of rule-based form-filling tools. To address these challenges, this study proposes KAFA, a knowledge-augmented form-filling agent for higher education services. KAFA integrates a large language model (DeepSeek), Chrome DevTools Protocol (CDP)-based web structure parsing, a multimodal personal knowledge base, tri-source collaborative retrieval, historical trajectory reuse, and adaptive error correction. Experiments on public recruitment forms, which share profile-oriented structures with many higher-education service forms, show that KAFA improves field matching and end-to-end form completion across different complexity levels. The results demonstrate the potential of combining large language models with structured, vector, and graph-based knowledge for robust form filling in education-oriented web services. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Knowledge-augmented form-filling agent for higher education services.
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  Data: <searchLink fieldCode="AR" term="%22Sang%2C+Haiwei%22">Sang, Haiwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> haiwei.sang@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Mengyun%22">Liu, Mengyun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> lmy4pub@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Tang%2C+Jing%22">Tang, Jing</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> tangjing202209@163.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Yuxiang%22">Yang, Yuxiang</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> gs.yuxiangyang22@gzu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Yuling%22">Chen, Yuling</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> ylchen3@gzu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Educational+Technology+in+Higher+Education%22">International Journal of Educational Technology in Higher Education</searchLink>. 7/6/2026, Vol. 23 Issue 1, p1-20. 20p.
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  Data: *<searchLink fieldCode="DE" term="%22Knowledge+base%22">Knowledge base</searchLink><br />*<searchLink fieldCode="DE" term="%22Universities+%26+colleges%22">Universities & colleges</searchLink><br />*<searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Data+extraction%22">Data extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Data+entry%22">Data entry</searchLink>
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  Data: Online academic and administrative services in higher education increasingly require students, faculty, and staff to complete structured forms across heterogeneous institutional platforms. These tasks are complicated by variations in field naming, page layout, validation rules, and dynamic interaction logic, which limits the reliability of rule-based form-filling tools. To address these challenges, this study proposes KAFA, a knowledge-augmented form-filling agent for higher education services. KAFA integrates a large language model (DeepSeek), Chrome DevTools Protocol (CDP)-based web structure parsing, a multimodal personal knowledge base, tri-source collaborative retrieval, historical trajectory reuse, and adaptive error correction. Experiments on public recruitment forms, which share profile-oriented structures with many higher-education service forms, show that KAFA improves field matching and end-to-end form completion across different complexity levels. The results demonstrate the potential of combining large language models with structured, vector, and graph-based knowledge for robust form filling in education-oriented web services. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 7/6/2026
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