  Figural analogy problems have long been a widely used format in human
intelligence tests. In the past four decades, more and more research has
investigated automatic item generation for figural analogy problems, i.e.,
algorithmic approaches for systematically and automatically creating such
problems. In cognitive science and psychometrics, this research can deepen our
understandings of human analogical ability and psychometric properties of
figural analogies. With the recent development of data-driven AI models for
reasoning about figural analogies, the territory of automatic item generation
of figural analogies has further expanded. This expansion brings new challenges
as well as opportunities, which demand reflection on previous item generation
research and planning future studies. This paper reviews the important works of
automatic item generation of figural analogies for both human intelligence
tests and data-driven AI models. From an interdisciplinary perspective, the
principles and technical details of these works are analyzed and compared, and
desiderata for future research are suggested.
