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Evaluating a large language model's ability to solve programming exercises from an introductory bioinformatics

Stephen R Piccolo¹, Paul Denny², Andrew Luxton-Reilly²

¹Department of Biology, Brigham Young University, Provo, Utah, United States of America.

Plos Computational Biology

|September 28, 2023

Summary

This summary is machine-generated.

Artificial intelligence tools like ChatGPT can successfully complete most introductory bioinformatics programming exercises. This suggests a need for updated teaching methods and potential collaboration between researchers and AI for coding tasks.

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Area of Science:

Life Sciences
Bioinformatics
Computer Science Education

Background:

Computer programming is essential for life scientists but challenging to learn.
Artificial intelligence (AI) advancements enable code generation from natural language prompts.
The potential of AI to assist or replace human coding efforts in life sciences is under investigation.

Purpose of the Study:

To evaluate the effectiveness of OpenAI's ChatGPT in solving programming tasks for life scientists.
To assess the performance of ChatGPT on introductory bioinformatics course exercises.

Main Methods:

184 programming exercises from an introductory bioinformatics course were used.
ChatGPT's ability to solve exercises was tested.
Natural language feedback was provided for exercises ChatGPT did not solve initially.

Main Results:

ChatGPT successfully solved 75.5% of exercises on the first attempt.
Within seven or fewer attempts, ChatGPT solved 97.3% of all exercises.
The AI model demonstrated significant capability in completing programming tasks.

Conclusions:

AI tools like ChatGPT show high proficiency in solving common life science programming problems.
Educational strategies and assessment methods in life sciences may require adaptation due to AI.
AI models offer potential as collaborative tools for life science researchers in coding.