AI versus human graders: A symbolic representation of the collaboration and conflict between artificial intelligence and human expertise in education.

AI vs. Human Graders: Are Large Language Models Ready to Take Over Education?

Theo Raines in Mind & Education June 2025 • 4 min read.

"A deep dive into the potential and limitations of ChatGPT in assessing student essays, revealing surprising insights for educators and students alike."

In higher education, grading remains a core yet demanding task. Educators face growing student populations and increasingly diverse assessments, prompting a search for innovative solutions to traditional, often biased, grading methods. Large Language Models (LLMs) like those powering ChatGPT offer a potential alternative, promising efficiency and objectivity. But how well do they perform in practice?

A recent study investigated the capabilities of Generative Pretrained Transformers (GPTs), specifically the GPT-4 model, in grading master-level student essays. By comparing GPT-4's assessments to those of university teachers, researchers uncovered critical insights into the efficacy and reliability of AI as a grading tool. The central question: Can GPT-4 provide accurate numerical grades for written essays in higher social science education?

This analysis delves into the study's methodology, key findings, and the broader implications for AI in education. We'll explore whether GPT-4 truly aligns with human grading standards, its potential biases, and the adjustments needed to enhance its adaptability and sensitivity to specific educational requirements. This is not just about technology; it's about the future of learning and assessment.

ChatGPT vs. Human Graders: A Head-to-Head Comparison

AI versus human graders: A symbolic representation of the collaboration and conflict between artificial intelligence and human expertise in education.

The study employed a sample of 60 anonymized master-level essays in political science, previously graded by university teachers. These grades served as a benchmark to evaluate GPT-4's performance. Researchers utilized a variety of instructions ('prompts') to explore variations in GPT-4's quantitative measures of predictive performance and interrater reliability. The goal was to understand how well GPT-4 could replicate human grading patterns under different conditions.

The investigation revealed several key findings:

Mean Score Alignment: GPT-4 closely aligns with human graders in terms of mean scores, suggesting it captures the overall quality of essays reasonably well.
Risk-Averse Grading: GPT-4 exhibits a conservative grading pattern, primarily assigning grades within a narrower middle range. It avoids extreme high or low grades, indicating a potential bias toward the average.
Low Interrater Reliability: GPT-4 demonstrates relatively low interrater reliability with human graders, evidenced by a Cohen's kappa of 0.18 and a percent agreement of 35%. This suggests significant discrepancies in how AI and humans interpret and evaluate essay quality.
Prompt Engineering Limitations: Adjustments to the grading instructions via prompt engineering do not significantly influence GPT-4's performance. This indicates that the AI predominantly evaluates essays based on generic characteristics like language quality and structural coherence, rather than adapting to nuanced assessment criteria.

In essence, while GPT-4 can provide a general sense of essay quality, it struggles to replicate the nuanced judgments of human graders. Its risk-averse approach and limited adaptability to specific grading criteria raise concerns about its suitability for high-stakes assessments.

The Future of AI in Education: Promise and Pitfalls

The study underscores the need for further development to enhance AI's adaptability and sensitivity to specific educational assessment requirements. While AI holds promise for reducing grading workload and providing resource-efficient assessment, significant improvements are needed to align its judgments with human raters. The challenge lies in enabling AI to move beyond generic essay characteristics and adapt to the detailed, nuanced criteria embedded within different prompts. As AI technology continues to evolve, it's crucial to address these limitations to ensure its responsible and effective integration into higher education.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2406.1651,

Title: Large Language Models In Student Assessment: Comparing Chatgpt And Human Graders

Subject: econ.gn q-fin.ec

Authors: Magnus Lundgren

Published: 24-06-2024

Everything You Need To Know

How does GPT-4 compare to human graders in terms of grading accuracy?

The study compared GPT-4's grading accuracy to that of university teachers using a sample of 60 master-level essays in political science. The key findings reveal that GPT-4 aligns with human graders in mean scores, indicating it can capture the overall quality of essays reasonably well. However, GPT-4 exhibits low interrater reliability, with a Cohen's kappa of 0.18 and a percent agreement of 35%. This suggests significant discrepancies between AI and human grading, and it struggles to replicate the nuanced judgments of human graders. This means, while GPT-4 provides a general sense of essay quality, it is not as accurate as human graders in providing grades that align with the grades of human graders.

What are the limitations of GPT-4 in grading essays, and what are the implications for its use in education?

GPT-4 demonstrates several limitations, including risk-averse grading, where it avoids extreme high or low grades, potentially reflecting a bias toward the average. It also shows low interrater reliability with human graders, indicating significant differences in how AI and humans assess essay quality. Furthermore, prompt engineering adjustments do not significantly influence GPT-4's performance, meaning it primarily evaluates essays based on generic characteristics like language quality and structural coherence. These limitations suggest that GPT-4 is not yet suitable for high-stakes assessments, requiring further development to enhance its adaptability to specific educational requirements. The implications are that AI's integration must be responsible and effective, addressing these limitations to ensure alignment with human grading standards.

What is 'prompt engineering' and how does it affect GPT-4's performance in grading?

Prompt engineering refers to the practice of crafting specific instructions or prompts to guide an AI model, like GPT-4, in performing a task. The study used various prompts to explore how different instructions influenced GPT-4's grading of essays. However, the results showed that prompt engineering did not significantly improve GPT-4's performance. This indicates that the AI predominantly relies on generic characteristics, such as language quality and structural coherence, rather than adapting to nuanced assessment criteria. Consequently, GPT-4's ability to tailor its grading to specific instructions is limited, highlighting a need for improvements in its adaptability and sensitivity to detailed assessment requirements.

What are the potential benefits of using Large Language Models (LLMs) like GPT-4 in grading student essays?

The potential benefits of using LLMs like GPT-4 in grading include reducing the grading workload for educators and offering a resource-efficient method of assessment. By automating part of the grading process, LLMs could free up educators' time, allowing them to focus on other critical aspects of teaching. Additionally, AI-powered grading could provide more consistent and objective assessments, potentially mitigating some of the biases inherent in human grading. This could lead to more equitable assessment practices. The use of LLMs may also offer scalability, allowing for the efficient grading of a large number of essays. However, these benefits are conditional on addressing the limitations, such as enhancing the AI's adaptability and sensitivity to specific educational requirements.

How does the study's findings influence the future of AI in higher education and the role of ChatGPT?

The study's findings underscore the need for further development to improve AI's adaptability and sensitivity to specific educational assessment requirements. While AI holds promise for reducing grading workload and providing resource-efficient assessment, significant improvements are needed to align its judgments with human raters. The challenge lies in enabling AI to move beyond generic essay characteristics and adapt to the detailed, nuanced criteria embedded within different prompts. The results suggest that, while ChatGPT, powered by GPT-4, can provide a general sense of essay quality, it struggles to replicate the nuanced judgments of human graders. This means that, for the future, responsible and effective integration of AI into education requires addressing these limitations to ensure its alignment with human grading standards. As AI technology continues to evolve, it's crucial to enhance its abilities for responsible and effective use in higher education.