  Multiple-choice questions (MCQs) offer the most promising avenue for skill
evaluation in the era of virtual education and job recruiting, where
traditional performance-based alternatives such as projects and essays have
become less viable, and grading resources are constrained. The automated
generation of MCQs would allow assessment creation at scale. Recent advances in
natural language processing have given rise to many complex question generation
methods. However, the few methods that produce deployable results in specific
domains require a large amount of domain-specific training data that can be
very costly to acquire. Our work provides an initial foray into MCQ generation
under high data-acquisition cost scenarios by strategically emphasizing
paraphrasing the question context (compared to the task). In addition to
maintaining semantic similarity between the question-answer pairs, our
pipeline, which we call AGenT Zero, consists of only pre-trained models and
requires no fine-tuning, minimizing data acquisition costs for question
generation. AGenT Zero successfully outperforms other pre-trained methods in
fluency and semantic similarity. Additionally, with some small changes, our
assessment pipeline can be generalized to a broader question and answer space,
including short answer or fill in the blank questions.
