Automatic item generation: foundations and machine learning-based approaches for assessments
Ruhan Circi, Juanita Hicks, Emmanuel Sikali
Abstract
This mini review summarizes the current state of knowledge about automatic item generation in the context of educational assessment and discusses key points in the item generation pipeline. Assessment is critical in all learning systems and digitalized assessments have shown significant growth over the last decade. This leads to an urgent need to generate more items in a fast and efficient manner. Continuous improvements in computational power and advancements in methodological approaches, specifically in the field of natural language processing, provide new opportunities as well as new challenges in automatic generation of items for educational assessment. This mini review asserts the need for more work across a wide variety of areas for the scaled implementation of AIG.