Thrilled by Your Progress! Large Language Models (GPT-4) No Longer
  Struggle to Pass Assessments in Higher Education Programming Courses
Jaromir Savelka Arav Agarwal Marshall An Chris Bogart Majd Sakr
This paper studies recent developments in large language models' (LLM)
abilities to pass assessments in introductory and intermediate Python
programming courses at the postsecondary level. The emergence of ChatGPT
resulted in heated debates of its potential uses (e.g., exercise generation,
code explanation) as well as misuses in programming classes (e.g., cheating).
Recent studies show that while the technology performs surprisingly well on
diverse sets of assessment instruments employed in typical programming classes
the performance is usually not sufficient to pass the courses. The release of
GPT-4 largely emphasized notable improvements in the capabilities related to
handling assessments originally designed for human test-takers. This study is
the necessary analysis in the context of this ongoing transition towards mature
generative AI systems. Specifically, we report the performance of GPT-4,
comparing it to the previous generations of GPT models, on three Python courses
with assessments ranging from simple multiple-choice questions (no code
involved) to complex programming projects with code bases distributed into
multiple files (599 exercises overall). Additionally, we analyze the
assessments that were not handled well by GPT-4 to understand the current
limitations of the model, as well as its capabilities to leverage feedback
provided by an auto-grader. We found that the GPT models evolved from
completely failing the typical programming class' assessments (the original
GPT-3) to confidently passing the courses with no human involvement (GPT-4).
While we identified certain limitations in GPT-4's handling of MCQs and coding
exercises, the rate of improvement across the recent generations of GPT models
strongly suggests their potential to handle almost any type of assessment
widely used in higher education programming courses. These findings could be
leveraged by educators and institutions to adapt the design of programming
assessments as well as to fuel the necessary discussions into how programming
classes should be updated to reflect the recent technological developments.
This study provides evidence that programming instructors need to prepare for a
world in which there is an easy-to-use widely accessible technology that can be
utilized by learners to collect passing scores, with no effort whatsoever, on
what today counts as viable programming knowledge and skills assessments.