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Cited by 188Artificial Intelligence in EducationEducational Technology and AssessmentIntelligent Tutoring Systems and Adaptive Learning

The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.

Applying large language models and chain-of-thought for automatic scoring

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Lee, Gyeong-Geon, Latif, Ehsan, Wu, Xuansheng, Liu, Ninghao, Zhai, Xiaoming·2024·Computers and Education: Artificial Intelligence
DOI 10.1016/j.caeai.2024.100213
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Abstract

This study investigates the application of large language models (LLMs), specifically GPT-3.5 and GPT-4, with Chain-of-Though (CoT) in the automatic scoring of student-written responses to science assessments. We focused on overcoming the challenges of accessibility, technical complexity, and lack of explainability that have previously limited the use of artificial intelligence-based automatic scoring tools among researchers and educators. With a testing dataset comprising six assessment tasks (three binomial and three trinomial) with 1650 student responses, we employed six prompt engineering strategies to automatically score student responses. The six strategies combined zero-shot or few-shot learning with CoT, either alone or alongside item stem and scoring rubrics, developed based on a novel approach, WRVRT (prompt writing, reviewing, validating, revising, and testing). Results indicated that few-shot (acc = 0.67) outperformed zero-shot learning (acc = 0.60), with 12.6% increase. CoT, when used without item stem and scoring rubrics, did not significantly affect scoring accuracy (acc = 0.60). However, CoT prompting paired with contextual item stems and rubrics proved to be a significant contributor to scoring accuracy (13.44% increase for zero-shot; 3.7% increase for few-shot). We found a more balanced accuracy across different proficiency categories when CoT was used with a scoring rubric, highlighting the importance of domain-specific reasoning in enhancing the effectiveness of LLMs in scoring tasks. We also found that GPT-4 demonstrated superior performance over GPT -3.5 in various scoring tasks when combined with the single-call greedy sampling or ensemble voting nucleus sampling strategy, showing 8.64% difference. Particularly, the single-call greedy sampling strategy with GPT-4 outperformed other approaches. This study also demonstrates the potential of LLMs in facilitating explainable and interpretable automatic scoring, emphasizing that CoT enhances accuracy and transparency, particularly when used with item stem and scoring rubrics.

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Metadata

Authors
Lee, G.-G., Latif, E., Wu, X., Liu, N., & Zhai, X.
Affiliations
University of Georgia (US)
ORCID
Venue
Computers and Education: Artificial Intelligence
Publisher
Elsevier BV
Published
2024-06
Volume
6
Pages
100213
ISSN
2666-920X (ISSN-L)
DOI
10.1016/j.caeai.2024.100213
OpenAlex
W4392202731
Cited by
186
References
58
Citations by year
2026: 80 · 2025: 73 · 2024: 33
FWCI
69.94
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
National Science Foundation (2101104)
Topics
  • Social Sciences › Education › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
automatic scoring, large language models, chain-of-thought prompting, GPT-4, GPT-3.5, prompt engineering, few-shot learning, zero-shot learning, rubrics, science assessment, student response, explainable AI, greedy sampling, nucleus sampling, ensemble voting, proficiency level

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