STEM departments are increasingly asked to include more writing in their courses, but they rarely have extra hours to grade it. Assigning a book like Joe Schwarcz's "Radar, Hula Hoops, and Playful Pigs" is a manageable first step, yet the grading burden still lands on faculty who are not trained as writing instructors. AI essay grading can reduce that burden, but only if it is adopted deliberately. A phased plan prevents confusion, builds trust among faculty, and produces evidence for decisions.

Start by clarifying the problem you are trying to solve. Is it turnaround time, consistency across sections, faculty workload, or the quality of feedback? Different goals call for different measures of success, and a department that tries to solve everything at once will struggle to know whether the tool is working.

Identify a small group of volunteers to lead a pilot. Choose faculty who are interested but not uncritical, and include at least one skeptic who will ask hard questions. A diverse pilot group produces more credible findings and makes it easier to bring reluctant colleagues along later.

Designing a pilot that produces real evidence

A good pilot is limited in scope and clear in purpose. Select one assignment, one or two courses, and a defined time frame. Agree on a shared rubric before starting, and decide how you will compare AI-assisted grading to your normal process, such as by hand grading a sample and comparing scores.

• Choose one writing assignment and agree on a rubric all pilot instructors will use.
• Grade a sample of essays by hand and compare results with AI-assisted scores.
• Track time spent grading per paper before and during the pilot.
• Collect short surveys from students on the usefulness and clarity of feedback.
• Hold a debrief meeting to discuss disagreements, surprises, and needed rubric changes.

Training faculty and setting expectations

Faculty need to understand what the tool does and does not do. Explain that it applies a rubric, drafts comments, and flags unusual papers, while final judgment remains with the instructor. Clear expectations prevent two common problems: over-trusting the output and rejecting the tool after one visible mistake.

Provide a short, practical orientation session with live examples using real student writing. Faculty are more convinced by seeing the tool handle their own assignments than by reading a feature list. Encourage them to bring difficult cases, since these reveal both strengths and limits.

Addressing faculty concerns openly

Common concerns include fairness, student privacy, academic freedom, and the fear that automation will devalue teaching. Address each directly. Explain how scores are reviewed, what data is kept, and how faculty can override any result, and emphasize that the goal is to free time for more meaningful interaction with students.

Make it clear that participation in the pilot does not commit anyone to permanent adoption. Allowing faculty to opt out or modify how they use the tool reduces resistance and encourages honest feedback about what works and what does not.

Measuring results and deciding whether to expand

At the end of the pilot, review the evidence against your original goals. If turnaround time dropped, agreement with hand grading was strong, and students found the feedback useful, expansion is justified. If not, identify whether the problem lies with the rubric, the training, or the tool itself before deciding next steps.

Document the findings in a short report for department leadership, including costs, time savings, and lessons learned. A clear record supports budget requests and helps other departments learn from your experience, which can extend the benefits well beyond the original pilot.