BibTeX:

@inproceedings{finnie-ansley2022robots,
author = {Finnie-Ansley, James and Denny, Paul and Becker, Brett A. and Luxton-Reilly, Andrew and Prather, James},
title = {The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming},
year = {2022},
isbn = {9781450396431},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3511861.3511863},
doi = {10.1145/3511861.3511863},
abstract = { Recent advances in artificial intelligence have been driven by an exponential growth in digitised data. Natural language processing, in particular, has been transformed by machine learning models such as OpenAI’s GPT-3 which generates human-like text so realistic that its developers have warned of the dangers of its misuse. In recent months OpenAI released Codex, a new deep learning model trained on Python code from more than 50 million GitHub repositories. Provided with a natural language description of a programming problem as input, Codex generates solution code as output. It can also explain (in English) input code, translate code between programming languages, and more. In this work, we explore how Codex performs on typical introductory programming problems. We report its performance on real questions taken from introductory programming exams and compare it to results from students who took these same exams under normal conditions, demonstrating that Codex outscores most students. We then explore how Codex handles subtle variations in problem wording using several published variants of the well-known “Rainfall Problem” along with one unpublished variant we have used in our teaching. We find the model passes many test cases for all variants. We also explore how much variation there is in the Codex generated solutions, observing that an identical input prompt frequently leads to very different solutions in terms of algorithmic approach and code length. Finally, we discuss the implications that such technology will have for computing education as it continues to evolve, including both challenges and opportunities.},
booktitle = {Australasian Computing Education Conference},
pages = {10–19},
numpages = {10},
keywords = {Codex, machine learning, neural networks, deep learning, artificial intelligence, code generation, OpenAI, AI, introductory programming, academic integrity, GPT-3, copilot, code writing, GitHub, novice programming, CS1},
location = {Virtual Event, Australia},
series = {ACE '22}
}

EndNote:

%0 Conference Paper 
%T The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming 
%@ 9781450396431 
%U https://doi.org/10.1145/3511861.3511863 
%R 10.1145/3511861.3511863 
%B Australasian Computing Education Conference 
%I Association for Computing Machinery 
%A James Finnie-Ansley 
%A Paul Denny 
%A Brett A. Becker 
%A Andrew Luxton-Reilly 
%A James Prather 
%D 2022 
%P 10–19 
%K OpenAI, GitHub, AI, neural networks, Codex, code writing, artificial intelligence, CS1, academic integrity, code generation, novice programming, GPT-3, deep learning, copilot, machine learning, introductory programming %C Virtual Event, Australia

ACM:

James Finnie-Ansley, Paul Denny, Brett A. Becker, Andrew Luxton-Reilly, and James Prather. 2022. The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming. In Australasian Computing Education Conference (ACE '22). Association for Computing Machinery, New York, NY, USA, 10–19. DOI:https://doi.org/10.1145/3511861.3511863