Department heads who are asked to evaluate AI grading tools often start with feature lists and pricing pages, but those comparisons say little about how a tool handles real student writing. A far more reliable method is to run each candidate tool on the same set of essays and compare the results. A shared novel assignment, such as essays on Gueule d'Ange, offers a natural test set.

Begin by selecting a sample of ten to fifteen essays that cover the range of quality in your classes. Include strong essays, average ones, and a few weak or unusual papers, and remove student names before testing. Have two teachers score the sample independently so you have a human baseline to compare against.

Next, give every tool the same rubric and the same instructions. Differences in results should come from the tool and not from differences in setup. Record the scores and comments each tool produces so the comparison is based on evidence rather than impressions.

Measure what matters to your department

Agreement with teacher scores is the obvious metric, but it is not the only one. Consider how specific the comments are, whether they point to actual passages in the essay, and whether they offer a clear next step. A tool that matches scores but produces generic comments may not help students improve.

• Score agreement with experienced teachers on the same essays
• Accuracy of feedback on French grammar and usage
• Specificity of comments tied to the student's own writing
• Handling of unusual or creative interpretations of the novel
• Ease of setting up rubrics and reviewing results

Test language handling directly

French departments have a particular concern that general purpose tools may not share: accuracy in the target language. Include essays with typical learner errors and check whether the tool identifies them correctly without flagging valid constructions as mistakes. False corrections can mislead students, so they deserve close attention in any evaluation.

Also test how the tool responds to essays that mix strong ideas with weak language, and the reverse. A fair tool should score these dimensions separately according to the rubric. If it lets language quality overwhelm the content score, it may not suit a literature course.

Consider workflow and privacy

Beyond accuracy, practical factors matter. Ask how essays are uploaded, how teachers review and edit feedback, and how easily results can be exported to your gradebook. Also confirm how student data is stored and whether it is used for any purpose beyond grading, since school policies often require clear answers.

Involve teachers in the pilot rather than deciding at the department level alone. Teachers who test a tool on their own classes will notice workflow issues that a demo never reveals. Their feedback also builds trust, which is essential if the tool is adopted more widely.

Make a decision you can defend

Summarize the results in a short document that records the method, the sample, and the findings for each tool. This gives administrators a clear basis for the recommendation and protects the department if questions arise later. A transparent process also makes it easier to revisit the decision when new tools appear.

Plan a short review after the first semester of use. Compare AI supported grades with teacher judgment on a small sample again and confirm that quality has held up. Ongoing checks keep the tool accountable and keep teachers in control of final grades.