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New GRASP pipeline automates grading of multi-topic essays

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a Graph-Retrieval Automated Scoring Pipeline (GRASP) designed to grade multi-topic science exams without requiring labeled training data. GRASP encodes reference answers into a FAISS vector index and constructs a semantic similarity graph. It then uses sentence count heuristics and a large language model to determine the number of topics answered in a student's essay, followed by a graph traversal method to retrieve relevant reference nodes. Finally, the Hungarian algorithm assigns reference nodes to question segments, and GPT-4.1-mini grades each segment. AI

IMPACT This research could improve the efficiency and accuracy of grading in educational settings, particularly for complex, multi-topic exams.

RANK_REASON The cluster contains an academic paper detailing a new method for automated essay grading. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New GRASP pipeline automates grading of multi-topic essays

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The cluster contains an academic paper detailing a new method for automated essay grading. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Saad Sajid Hashmi ·

    GRASP: Graph-Retrieval Automated Scoring Pipeline for Label-Free Multi-Topic Essay Grading

    Automated short-answer grading research has historically focused on exams consisting solely of questions pertaining to a single topic. Automatic grading of exams containing questions about more than one topic remains less explored. In this work, a Graph-Retrieval Automated Scorin…

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