  Reading, understanding and explaining code have traditionally been important
skills for novices learning programming. As large language models (LLMs) become
prevalent, these foundational skills are more important than ever given the
increasing need to understand and evaluate model-generated code. Brand new
skills are also needed, such as the ability to formulate clear prompts that can
elicit intended code from an LLM. Thus, there is great interest in integrating
pedagogical approaches for the development of both traditional coding
competencies and the novel skills required to interact with LLMs. One effective
way to develop and assess code comprehension ability is with ``Explain in plain
English'' (EiPE) questions, where students succinctly explain the purpose of a
fragment of code. However, grading EiPE questions has always been difficult
given the subjective nature of evaluating written explanations and this has
stifled their uptake. In this paper, we explore a natural synergy between EiPE
questions and code-generating LLMs to overcome this limitation. We propose
using an LLM to generate code based on students' responses to EiPE questions --
not only enabling EiPE responses to be assessed automatically, but helping
students develop essential code comprehension and prompt crafting skills in
parallel. We investigate this idea in an introductory programming course and
report student success in creating effective prompts for solving EiPE
questions. We also examine student perceptions of this activity and how it
influences their views on the use of LLMs for aiding and assessing learning.
