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20232026
most citedExploring Generative AI assisted feedback writing for students' written responses to a physics conceptual question with prompt engineering and few-shot learning

100 citations · 132 across the 6 of their papers we have counts for

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Showing 2024Show all

3 papers · 1 filter

cs.CY2024★ 2 cited

Atomic Learning Objectives Labeling: A High-Resolution Approach for Physics Education

Naiming Liu, Shashank Sonkar, Debshila Basu Mallick +2

This paper introduces a novel approach to create a high-resolution "map" for physics learning: an "atomic" learning objectives (LOs) system designed to capture detailed cognitive p…

physics.ed-ph2024★ 25 cited

Using Large Language Models to Assign Partial Credit to Students' Explanations of Problem-Solving Process: Grade at Human Level Accuracy with Grading Confidence Index and Personalized Student-facing Feedback

Zhongzhou Chen, Tong Wan

This study examines the feasibility and potential advantages of using large language models, in particular GPT-4o, to perform partial credit grading of large numbers of student wri…

physics.ed-ph2024

Achieving Human Level Partial Credit Grading of Written Responses to Physics Conceptual Question using GPT-3.5 with Only Prompt Engineering

Zhongzhou Chen, Tong Wan

Large language modules (LLMs) have great potential for auto-grading student written responses to physics problems due to their capacity to process and generate natural language. In…