Can AI grade your essays? A comparative analysis of large language models and teacher ratings in multidimensional essay scoring

The manual assessment and grading of student writing is a time-consuming yet critical task for teachers. Recent developments in generative AI offer potential solutions to facilitate essay-scoring tasks for teachers. In our study, we evaluate the performance (e.g. alignment and reliability) of both open-source and closed-source LLMs in assessing German student essays, comparing their evaluations to those of 37 teachers across 10 pre-defined criteria (i.e., plot logic, expression). A corpus of 20 real-world essays from Year 7 and 8 students was analyzed using five LLMs: GPT-3.5, GPT-4, o1-preview, LLaMA 3-70B, and Mixtral 8x7B, aiming to provide in-depth insights into LLMs’ scoring capabilities. Closed-source GPT models outperform open-source models in both internal consistency and alignment with human ratings, particularly excelling in language-related criteria. The o1 model outperforms all other LLMs, achieving Spearman’s r =.74 with human assessments in the Overall score, and an internal consistency of ICC =.80, though biased towards higher scores. These findings indicate that LLM-based assessment can be a useful tool to reduce teacher workload by supporting the evaluation of essays, especially with regard to language-related criteria. However, due to their tendency to overrate and their remaining issues to capture the content quality, the models require further refinement.