NLP-powered automatic question generation (QG) techniques carry great
pedagogical potential of saving educators' time and benefiting student
learning. Yet, QG systems have not been widely adopted in classrooms to date.
In this work, we aim to pinpoint key impediments and investigate how to improve
the usability of automatic QG techniques for educational purposes by
understanding how instructors construct questions and identifying touch points
to enhance the underlying NLP models. We perform an in-depth need finding study
with 11 instructors across 7 different universities, and summarize their
thought processes and needs when creating questions. While instructors show
great interests in using NLP systems to support question design, none of them
has used such tools in practice. They resort to multiple sources of
information, ranging from domain knowledge to students' misconceptions, all of
which missing from today's QG systems. We argue that building effective
human-NLP collaborative QG systems that emphasize instructor control and
explainability is imperative for real-world adoption. We call for QG systems to
provide process-oriented support, use modular design, and handle diverse
sources of input.

本文重点探索了如何以教育为目的改进自动化问题生成技术，通过了解教师如何构建问题并确认增强 NLP 模型的触点来提高其可用性。作者观察到教师在使用 NLP 系统来支持问题设计方面表现出极大的兴趣，但实践中仍未被广泛采用。因此，作者呼吁建立强调教师控制和可解释性的有效人 - NLP 协作的 QG 系统，提供过程支持，使用模块化设计，处理各种输入来源。