As concept maps gain prominence in education, ensuring reliable and efficient assessment methods becomes increasingly important. However, automating this process remains a complex problem. Due to their free-form nature, assessing a concept map is subjective, as there is no single “correct” way to design one for a given topic. To address these challenges, this systematic literature review identifies techniques and criteria currently employed to automate the evaluation of concept maps. In addition to conventional systematic review methods, the study utilized DeepSeek as an auxiliary tool for paper selection, validating its results against selections made by two independent researchers via the Kappa test. The findings highlight recent advancements in evaluation techniques for using AI in qualitative assessment. Still, gaps persist in assessing collaborative and multilevel educational contexts. Additionally, the field lacks standardized evaluation criteria, including established datasets or universally accepted metrics for comparing the performance of different automated assessment approaches. By synthesizing current practices in automated concept map evaluation, this study aims to foster discussions on standardizing evaluation metrics and to provide a comprehensive overview of the state of the art in this evolving domain.