A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in
  Programming Education
Jacob Doughty Zipiao Wan Anishka Bompelli Jubahed Qayum Taozhi Wang Juran Zhang Yujia Zheng Aidan Doyle Pragnya Sridhar Arav Agarwal Christopher Bogart Eric Keylor Can Kultur Jaromir Savelka Majd Sakr
There is a constant need for educators to develop and maintain effective
up-to-date assessments. While there is a growing body of research in computing
education on utilizing large language models (LLMs) in generation and
engagement with coding exercises, the use of LLMs for generating programming
MCQs has not been extensively explored. We analyzed the capability of GPT-4 to
produce multiple-choice questions (MCQs) aligned with specific learning
objectives (LOs) from Python programming classes in higher education.
Specifically, we developed an LLM-powered (GPT-4) system for generation of MCQs
from high-level course context and module-level LOs. We evaluated 651
LLM-generated and 449 human-crafted MCQs aligned to 246 LOs from 6 Python
courses. We found that GPT-4 was capable of producing MCQs with clear language,
a single correct choice, and high-quality distractors. We also observed that
the generated MCQs appeared to be well-aligned with the LOs. Our findings can
be leveraged by educators wishing to take advantage of the state-of-the-art
generative models to support MCQ authoring efforts.