Assisting the Grading of a Handwritten General Chemistry Exam with Artificial Intelligence

arXiv:2509.10591 · cs.CY, cs.AI · Submitted 2025-09-12 · Read on arXiv

cs.CY, cs.AI

Submitted: 2025-09-12

Updated: 2025-11-10

License: http://creativecommons.org/licenses/by/4.0/

The gist: We explore the effectiveness and reliability of an artificial intelligence (AI)-based grading system for a handwritten general chemistry exam, comparing AI-assigned scores to human grading across

Terminology

Accuracy — 18× in this paper · explained in 1 episode
False Negative — 7× in this paper
False Positive — 6× in this paper
Alignment — 5× in this paper · explained in 1 episode
Artificial Intelligence (AI) — 3× in this paper · explained in 2 episodes
Human-in-the-Loop — 3× in this paper · explained in 3 episodes
Aggregation — 2× in this paper · explained in 1 episode
Calibration — 2× in this paper · explained in 7 episodes
Consistency — 2× in this paper · explained in 3 episodes
F1 Score — 2× in this paper · explained in 3 episodes
Generalizability — 2× in this paper · explained in 6 episodes
Generative AI — 2× in this paper · explained in 2 episodes
Item Response Theory — 2× in this paper
Systematic Bias — 2× in this paper · explained in 3 episodes
True Negative — 2× in this paper
True Positive — 2× in this paper
Accountability — 1× in this paper · explained in 2 episodes
Benchmarking — 1× in this paper · explained in 3 episodes
Commentary — 1× in this paper · explained in 2 episodes
Deferral — 1× in this paper · explained in 2 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Fine-Tuning — 1× in this paper · explained in 5 episodes
Hybrid Approach — 1× in this paper · explained in 4 episodes
Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
Transformer — 1× in this paper · explained in 2 episodes
Verification — 1× in this paper · explained in 2 episodes

Abstract

We explore the effectiveness and reliability of an artificial intelligence (AI)-based grading system for a handwritten general chemistry exam, comparing AI-assigned scores to human grading across various types of questions. Exam pages and grading rubrics were uploaded as images to account for chemical reaction equations, short and long open-ended answers, numerical and symbolic answer derivations, drawing, and sketching in pencil-and-paper format. Using linear regression analyses and psychometric evaluations, the investigation reveals high agreement between AI and human graders for textual and chemical reaction questions, while highlighting lower reliability for numerical and graphical tasks. The findings emphasize the necessity for human oversight to ensure grading accuracy, based on selective filtering. The results indicate promising applications for AI in routine assessment tasks, though careful consideration must be given to student perceptions of fairness and trust in integrating AI-based grading into educational practice.

Assisting the Grading of a Handwritten General Chemistry Exam with Artificial Intelligence

Terminology

Abstract

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