Large Language Models for Assisting American College Applications
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866912911155265536 |
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| author | Liu, Zhengliang You, Weihang Shu, Peng Chen, Junhao Pan, Yi Jiang, Hanqi Li, Yiwei Ding, Zhaojun Cao, Chao Li, Xinliang Zhou, Yifan Zhang, Ruidong Xu, Shaochen Ruan, Wei Zhao, Huaqin Zhu, Dajiang Liu, Tianming |
| author_facet | Liu, Zhengliang You, Weihang Shu, Peng Chen, Junhao Pan, Yi Jiang, Hanqi Li, Yiwei Ding, Zhaojun Cao, Chao Li, Xinliang Zhou, Yifan Zhang, Ruidong Xu, Shaochen Ruan, Wei Zhao, Huaqin Zhu, Dajiang Liu, Tianming |
| contents | American college applications require students to navigate fragmented admissions policies, repetitive and conditional forms, and ambiguous questions that often demand cross-referencing multiple sources. We present EZCollegeApp, a large language model (LLM)-powered system that assists high-school students by structuring application forms, grounding suggested answers in authoritative admissions documents, and maintaining full human control over final responses. The system introduces a mapping-first paradigm that separates form understanding from answer generation, enabling consistent reasoning across heterogeneous application portals. EZCollegeApp integrates document ingestion from official admissions websites, retrieval-augmented question answering, and a human-in-the-loop chatbot interface that presents suggestions alongside application fields without automated submission. We describe the system architecture, data pipeline, internal representations, security and privacy measures, and evaluation through automated testing and human quality assessment. Our source code is released on GitHub (https://github.com/ezcollegeapp-public/ezcollegeapp-public) to facilitate the broader impact of this work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15850 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Assisting American College Applications Liu, Zhengliang You, Weihang Shu, Peng Chen, Junhao Pan, Yi Jiang, Hanqi Li, Yiwei Ding, Zhaojun Cao, Chao Li, Xinliang Zhou, Yifan Zhang, Ruidong Xu, Shaochen Ruan, Wei Zhao, Huaqin Zhu, Dajiang Liu, Tianming Computation and Language American college applications require students to navigate fragmented admissions policies, repetitive and conditional forms, and ambiguous questions that often demand cross-referencing multiple sources. We present EZCollegeApp, a large language model (LLM)-powered system that assists high-school students by structuring application forms, grounding suggested answers in authoritative admissions documents, and maintaining full human control over final responses. The system introduces a mapping-first paradigm that separates form understanding from answer generation, enabling consistent reasoning across heterogeneous application portals. EZCollegeApp integrates document ingestion from official admissions websites, retrieval-augmented question answering, and a human-in-the-loop chatbot interface that presents suggestions alongside application fields without automated submission. We describe the system architecture, data pipeline, internal representations, security and privacy measures, and evaluation through automated testing and human quality assessment. Our source code is released on GitHub (https://github.com/ezcollegeapp-public/ezcollegeapp-public) to facilitate the broader impact of this work. |
| title | Large Language Models for Assisting American College Applications |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.15850 |