  Text-based open-ended questions in academic formative and summative
assessments help students become deep learners and prepare them to understand
concepts for a subsequent conceptual assessment. However, grading text-based
questions, especially in large courses, is tedious and time-consuming for
instructors. Text processing models continue progressing with the rapid
development of Artificial Intelligence (AI) tools and Natural Language
Processing (NLP) algorithms. Especially after breakthroughs in Large Language
Models (LLM), there is immense potential to automate rapid assessment and
feedback of text-based responses in education. This systematic review adopts a
scientific and reproducible literature search strategy based on the PRISMA
process using explicit inclusion and exclusion criteria to study text-based
automatic assessment systems in post-secondary education, screening 838 papers
and synthesizing 93 studies. To understand how text-based automatic assessment
systems have been developed and applied in education in recent years, three
research questions are considered. All included studies are summarized and
categorized according to a proposed comprehensive framework, including the
input and output of the system, research motivation, and research outcomes,
aiming to answer the research questions accordingly. Additionally, the typical
studies of automated assessment systems, research methods, and application
domains in these studies are investigated and summarized. This systematic
review provides an overview of recent educational applications of text-based
assessment systems for understanding the latest AI/NLP developments assisting
in text-based assessments in higher education. Findings will particularly
benefit researchers and educators incorporating LLMs such as ChatGPT into their
educational activities.
