We examined the extent to which automated written expression curriculum-based measurement (aWE-CBM) can be accurately used to computer score student writing samples for screening and progress monitoring. Students (n = 174) with learning difficulties in Grades 1 to 12 who received 1:1 academic tutoring through a community-based organization completed narrative writing samples in the fall and spring across two academic years. The samples were evaluated using four automated and hand-calculated WE-CBM scoring metrics. Results indicated automated and hand-calculated scores were highly correlated at all four timepoints for counts of total words written (rs = 1.00), words spelled correctly (rs = 0.99-1.00), correct word sequences (CWS; rs = 0.96-0.97), and correct minus incorrect word sequences (CIWS; rs = 0.86-0.92). For CWS and CIWS, however, automated scores systematically overestimated hand-calculated scores, with an unacceptable amount of error for CIWS for some types of decisions. These findings provide preliminary evidence that aWE-CBM can be used to efficiently score narrative writing samples, potentially improving the feasibility of implementing multi-tiered systems of support in which the written expression skills of large numbers of students are screened and monitored.