Performance Comparison: Human vs. GPT-3.5 vs. GPT-4 in University Coding Course

Alapfogalmak

AI-generated work closely approaches but remains detectable by human evaluators in university-level physics coding assignments.

Kivonat

This study compares the performance of students, GPT-3.5, and GPT-4 in physics coding assignments at Durham University. It evaluates AI contributions against solely student work and a mixed category, highlighting the detectability of AI-generated content by human markers.

Abstract:

• Compared ChatGPT variants GPT-3.5 and GPT-4 with prompt engineering to student submissions.
• Students outperformed AI submissions statistically significantly.
• Blinded markers accurately identified authorship as 'Definitely Human' or 'Definitely AI'.

Introduction:

• Coding courses are essential in university curricula globally.
• Study focuses on AI's impact on practical coding curriculum in physics degree at Durham University.

Methodology:

• Assessing Large Language Models (LLMs) effectiveness using blinded marking approach.
• Physics coding assessments emphasize plot quality and code performance for simulations.

Results:

• Students outperformed all AI categories, with GPT-4 scoring highest among AIs.
• Prompt engineering significantly improved scores for both GPT models.

Discussion:

• LLMs have not surpassed human proficiency yet in physics coding assignments.
• Unique design choices by students differentiate their work from AI-generated content.

Limitations:

• Pre-processing plays a crucial role in preparing AI for tasks, potentially affecting output quality.

Conclusion:

• GPT models show improvement over time but have not surpassed human capabilities yet.

Statisztikák

Students averaged 91.9% (SE:0.4), surpassing the highest performing AI submission category, GPT-4 with prompt engineering, which scored 81.1% (SE:0.8) - a statistically significant difference (p = 2.482 × 10−10).
Prompt engineering significantly improved scores for both GPT-4 (p = 1.661 × 10−4) and GPT-3.5 (p = 4.967 × 10−9).
Blinded markers accurately identified the authorship, with an average accuracy rate of 85.3%.

Idézetek

"Students averaged 91.9%, surpassing the highest performing AI submission category."
"Prompt engineering significantly improved scores for both GPT models."

Főbb Kivonatok

A comparison of Human, GPT-3.5, and GPT-4 Performance in a University-Level Coding Course

by Will Yeadon,... : arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16977.pdf

Mélyebb kérdések

How might the integration of AI into educational practices impact traditional teaching methods?

The integration of AI into educational practices has the potential to revolutionize traditional teaching methods in several ways. Firstly, AI can provide personalized learning experiences tailored to individual student needs and learning styles. By analyzing vast amounts of data on student performance, AI algorithms can identify areas where students may be struggling and offer targeted interventions or additional resources to support their learning.
Furthermore, AI can automate routine tasks such as grading assignments and providing feedback, freeing up valuable time for educators to focus on more interactive and engaging aspects of teaching. This automation can also lead to more consistent and objective assessment processes across different students.
Additionally, AI-powered tools like chatbots or virtual assistants can enhance student engagement by providing instant support outside of regular classroom hours. These tools can answer questions, provide explanations, or even facilitate collaborative problem-solving activities among students.
Overall, the integration of AI into education has the potential to make learning more efficient, effective, and engaging for both students and teachers alike.

How ethical considerations arise from using prompt engineering to enhance AI performance?

Using prompt engineering to enhance AI performance raises several ethical considerations that need careful attention. One key concern is transparency - if prompt engineering significantly influences an AI model's output or behavior but is not disclosed transparently, it could mislead users about the true capabilities of the system. This lack of transparency may erode trust in AI systems and raise concerns about accountability.
Another ethical consideration is fairness - if prompt engineering disproportionately benefits certain groups over others (e.g., based on language proficiency or cultural background), it could exacerbate existing inequalities in access to education or opportunities for academic success.
Moreover, there are concerns about authenticity - when prompt engineering makes an AI-generated work closely resemble human-created content without clear attribution, it blurs the lines between what is genuinely produced by humans versus machines. This lack of clarity raises questions about intellectual property rights and academic integrity.
To address these ethical considerations effectively, developers should prioritize transparency in how they use prompt engineering techniques while ensuring fairness in how these enhancements impact diverse user groups. Additionally, clear guidelines on attributing authorship should be established when presenting work generated with enhanced prompts by AIs.

How unique design choices by students be leveraged to distinguish human-created content from generated work?

Unique design choices made by students play a crucial role in distinguishing human-created content from generated work.
One way this distinction manifests is through visual aesthetics – such as color schemes used in plots – which often reflect personal preferences or creative decisions unique to individual students.
By leveraging these distinct design elements as markers for authenticity,
markers evaluating submissions can look for deviations from standard templates
or default settings commonly employed by AIs.
Additionally,
students' boldness
in making unconventional design choices sets their work apart
from potentially standardized outputs
generated by AIs.
These variations serve as indicators that help evaluators differentiate between human-authored content
and machine-generated material.
Educators could emphasize encouraging creativity
and originality within assignments,
as well as incorporating open-ended tasks that allow room for diverse interpretations
and innovative solutions beyond what typical algorithms might produce.
Ultimately,
by recognizing
the value placed on individual expression
through unique design choices,
evaluators gain insights into identifying authentic student contributions amidst a sea
of automated outputs generated through .
This approach underscores not only the importance
of fostering creativity within educational contexts but also highlights how such creativity serves as a hallmark distinguishing factor between human-authored works