Schools implementing AI grading often report that teachers love it and have more time. But the real question is: do students write better? Without measurement, you can't answer that question definitively. Building a simple evaluation plan helps you track actual impact on student outcomes.

The good news is that measuring impact is feasible. You don't need complex research designs. Simple before-and-after comparisons, data from your existing assessments, and tracking growth over time can all provide evidence of impact.

Key Outcomes to Measure

• Writing quality: Compare essay scores before AI grading was implemented to scores after. Do scores improve? Disaggregate by student group. Do all students improve, or just some?
• Writing frequency: Do students write more when feedback is faster and less burdensome to grade? Do teachers assign more writing assignments?
• Revision rate: Do students revise more with faster feedback available? This might show up as higher rates of students submitting draft two when the option is available.
• Standardized writing assessments: If your school administers standardized writing exams (state tests, AP exams, college placement exams), track trends. Is performance improving?
• Writing growth over time: For individual students, are they improving across the year more than they did in previous years? Disaggregate by student group.
• Engagement and motivation: Do student surveys suggest higher engagement with writing? Are reluctant writers more willing to attempt assignments?

Simple Before-and-After Comparison

The most straightforward evaluation: compare student writing quality before and after AI grading implementation. Use your existing rubric. Gather a sample of essays from the same classes in the year before implementation and the year after. Have a teacher (or group of teachers) blind-review and score them. Did scores improve? How much? For whom? This simple comparison provides meaningful evidence.

Be careful about confounds. If you also changed your curriculum, improved your teaching, or had different students, these factors affect outcomes. Try to isolate the impact of AI grading by holding other factors constant, or by explicitly measuring and accounting for other changes.

Tracking Individual Student Growth

A more sophisticated approach: track growth trajectories for individual students. In the year before AI grading, what was the average student improvement from first to last essay? In the year with AI grading, what is the improvement? Are students improving faster? Use value-added models that account for starting-point differences across classes. This type of analysis shows not just whether students ended up stronger, but whether they grew more.

Disaggregating by Student Subgroups

Measure whether AI grading benefits all students equally or whether some groups benefit more. Do multilingual learners show improvement? Do students with disabilities? Do high-achieving students and struggling students both improve, or mainly one group? If you find disparities in impact, investigate. Maybe AI feedback is particularly valuable for certain students. Maybe it's less effective for others, suggesting a need for adjustment.

Measuring Engagement and Motivation Indirectly

Student motivation is hard to measure directly, but you can measure proxies. Do more students opt into writing-heavy classes? Do students revise more when offered the opportunity? Do students ask more questions about feedback? Do fewer students leave blank assignments? These behaviors suggest engagement is improving.

Qualitative Feedback From Students and Teachers

Don't rely only on quantitative measures. Survey students and teachers. What's working? What's frustrating? Has anything unexpected happened? Qualitative feedback often reveals impacts that numbers alone don't capture. A student saying "I understand my feedback now because it's specific instead of just a grade" is valuable evidence that the system is working.

Communicating Results and Making Adjustments

Once you've measured impact, share the results. If outcomes improved, celebrate. If they didn't, don't panic. Instead, investigate. Maybe the tool needs reconfiguration. Maybe teachers need better training. Maybe rubrics need adjustment. Data is a guide to improvement, not a judgment. Use it to iteratively improve your practice.