Content-based automated scoring has been applied in a variety of science domains. However, many prior applications involved simplified scoring rubrics without considering rubrics representing multiple levels of understanding. This study tested a concept-based scoring tool for content-based scoring, c-rater™, for four science items with rubrics aiming to differentiate among multiple levels of understanding. The items showed moderate to good agreement with human scores. The findings suggest that automated scoring has the potential to score constructed-response items with complex scoring rubrics, but in its current design cannot replace human raters. This article discusses sources of disagreement and factors that could potentially improve the accuracy of concept-based automated scoring.