Introducing AI grading across a department can feel risky, especially when faculty have mixed feelings about the technology. One practical way to reduce that risk is to pilot the tool on a single shared unit with a common assignment. A reading unit built around Murder City, with a standard essay prompt and rubric, provides an ideal testing ground.

A shared assignment allows direct comparison across instructors. Because everyone is grading the same prompt with the same rubric, differences in scores and comments are easier to interpret. This makes it possible to evaluate the tool's performance and to identify where faculty judgment diverges.

Start small. Selecting a handful of volunteer instructors, rather than requiring everyone to participate, creates a group of informed advocates and honest critics. Their experiences provide valuable information for broader adoption and help the department avoid mistakes at scale.

Define Success Before the Pilot Begins

A pilot is only useful if it has clear goals. Decide in advance which outcomes matter, such as time saved per paper, speed of feedback, agreement with faculty scores, and teacher satisfaction. Writing these down prevents the evaluation from drifting toward whatever result seems most convenient.

• Establish a baseline for average grading time per essay before the pilot.
• Choose a shared rubric and prompt for all participating instructors.
• Compare tool-assisted scores with independent faculty scores on a sample.
• Collect instructor feedback on usability, accuracy, and trust.
• Review student reactions to the quality and usefulness of feedback.

Train Faculty and Set Expectations

Faculty need to understand what the tool does, what it does not do, and what is expected of them. A short training session covering how to upload a rubric, review output, and edit feedback can prevent confusion. Emphasizing that the instructor remains responsible for final grades helps address concerns about automation.

Clear communication with students is equally important. Students should know how their essays are being evaluated and that a teacher reviews the feedback. Transparency builds trust and reduces the chance of misunderstandings or complaints.

Evaluate Results Honestly

At the end of the pilot, review the data with an open mind. If the tool saved time and produced scores consistent with faculty judgment, that is a strong signal. If significant disagreements arose, examine whether the rubric was unclear, the tool misread certain kinds of arguments, or individual instructors applied different standards.

Qualitative feedback matters too. Instructors can describe where the tool helped and where it fell short, and those observations can shape training and policy. A frank discussion at this stage prevents unrealistic expectations and informs better decisions.

Scale Thoughtfully

If the pilot succeeds, expansion should be gradual. Add units, courses, or instructors in stages, continuing to monitor results as the scale increases. Maintaining the same evaluation practices ensures that problems are caught early.

Departments should also establish ongoing governance, including policies on data privacy, acceptable use, and review of the tool's performance each term. A clear owner for these responsibilities keeps the initiative from fading after the initial enthusiasm. Over time, the department can build a mature practice in which technology supports teachers rather than replaces their expertise.