  As artificial intelligence (AI) technologies become increasingly powerful and
prominent in society, their misuse is a growing concern. In educational
settings, AI technologies could be used by students to cheat on assignments and
exams. In this paper we explore whether transformers can be used to solve
introductory level programming assignments while bypassing commonly used AI
tools to detect similarities between pieces of software. We find that a student
using GPT-J [Wang and Komatsuzaki, 2021] can complete introductory level
programming assignments without triggering suspicion from MOSS [Aiken, 2000], a
widely used software similarity and plagiarism detection tool. This holds
despite the fact that GPT-J was not trained on the problems in question and is
not provided with any examples to work from. We further find that the code
written by GPT-J is diverse in structure, lacking any particular tells that
future plagiarism detection techniques may use to try to identify
algorithmically generated code. We conclude with a discussion of the ethical
and educational implications of large language models and directions for future
research.
