高校行政人员或科研项目管理者终于有了可落地的AI用例——LLM自动评分+理由输出，把数周评审压缩到几小时，做大规模申请筛选的团队可以直接参考这套工作流。
普渡大学暑期本科研究项目（SURF）每年收到数千份申请，传统人工评审耗时数周。研究人员开发了基于GPT-4o、GPT-5-mini和GPT-5.2的LLM工具，对1200份个人陈述进行结构化评分（六项子标准，0-3分）。GPT-5.2处理全部申请仅需4.6小时，平均每份14秒，且评分一致性最高。项目协调员结合LLM输出的分数和理由，在4小时内完成终审，而此前需要数周协调。该工具在低分申请上评分分歧较大，但整体显著缩短了评审周期。
Using Large Language Models to Support High Volume Application Review for an Undergraduate Research Program
Undergraduate research programs such as the Summer Undergraduate Research Fellowship (SURF) at Purdue University receive thousands of applications every year, requiring significant time and effort for program staff to evaluate each submission consistently and within tight timelines. This work-in-progress paper describes the development and initial deployment of a large language model (LLM)-based tool to assist in the evaluation of approximately 1,200 student Statements of Purpose (SoPs) for the SURF 2026 cycle at Purdue University. The workflow utilizes OpenAI GPT models (GPT-4o, GPT-5-mini, and GPT-5.2) and uses a structured rubric across six subcategories, each scored on a 0-3 scale. A few SoPs, graded by program staff, were used to tune the model responses. The model prompt was designed to generate both numerical scores, rationales (including positive and negative aspects) and short excerpts from each submission. Using GPT-5.2, the full batch of 1,200 SoPs was processed in approximately 4.6 hours of compute time, averaging roughly 14 seconds per SoP (with per-SoP timing varying with SoP length, which ranged from 500 to 2,000 words). Notable differences in rubric adherence were observed across model versions, with GPT-5.2 adhering most closely. Disagreement in model scores was more pronounced for lower-scoring submissions. The LLM outputs replicated the role previously played by distributed human graders, providing the program coordinator with scored and rationale-annotated outputs for the entire applicant pool. The program coordinator then reviewed these outputs alongside each applicant's SoP, applying the same downstream office criteria used in prior SURF cycles, to produce a shortlist of strong candidates. This coordinator review was completed in approximately 4 hours, compared to the multi-week coordination effort required in prior program cycles.