The U.S. Department of Education finalized a rule in April, effective in mid-May, that lets it prioritize grant applications for projects expanding the understanding or appropriate, ethical use of artificial intelligence in education. The listed K-12 priorities include professional development for educators, AI and computer science instruction, personalized learning tools, tutoring quality, and reducing administrative workload through automation. The agency also said it would consider whether and how to include evidence requirements in each competition.

For a district or college that wants to pilot rubric-based writing feedback, this is both an opportunity and a trap. Applications that describe a tool without describing how success will be measured tend to read as shopping lists, and reviewers have grown skeptical of broad promises about transformation. A tight evidence plan shows that the team understands the difference between a product and an intervention.

The sections below describe the pieces of an evidence plan that fit a writing feedback project of modest size. They apply equally to a single department seeking a small internal grant and to a district preparing a federal proposal. Each piece is small enough to write in an afternoon, and together they show reviewers that the team has thought about how the pilot will be judged, not only how it will be launched.

Define the problem in terms of student writing

Start with a specific, measurable problem, such as students receiving written feedback on only one of four major essays per term, or a two-week turnaround that leaves too little time for revision. Describe the current state with numbers from your own records, and name the group of students and teachers affected. Reviewers respond to a problem stated plainly more than to a general claim that grading is burdensome.

• State the writing problem using your own local numbers
• Pick two or three measurable outcomes such as turnaround time and revision rates
• Compare pilot sections with business-as-usual sections on a shared rubric
• Describe teacher review, moderation, and accuracy sampling
• Plan how results will be reported and what happens after funding ends

Choose outcomes you can actually measure

Pick two or three outcomes tied to the problem. Examples include turnaround time for feedback, the percentage of students who submit a revision, and growth in rubric scores between a first and second draft. Avoid outcomes that depend on satisfaction alone, since a pleasant experience does not prove learning. Decide in advance how each will be collected and who will collect it.

Include a comparison where possible. A common approach is to pilot the tool in some sections while others continue as usual, and compare results on a shared rubric. A modest comparison, honestly described, is more credible than an ambitious design that cannot be carried out, which also gives the team something concrete to discuss with school boards and trustees when the pilot ends.

Describe the human side of the workflow

Explain how teachers will review and edit machine-generated feedback before students see it, how scores will be moderated, and what training teachers will receive. Reviewers want to see that the tool supports professional judgment and does not replace it. Include a plan for sampling a share of responses to check accuracy and consistency, with a threshold that triggers retraining or adjustment.

Address privacy, consent, and bias in plain language. State what student data is collected, where it is stored, and whether it is used to train models, and describe how results will be examined for differences across student groups. These details matter to families as well as funders, and a short, plain statement in the proposal tends to build trust more effectively than pages of legal language.

Plan for reporting and for what happens after the grant

Commit to a timeline for interim and final reports, and to sharing results even if they are mixed. Honest findings about what did not work are valuable and strengthen the credibility of the applicant. Describe how the findings will inform decisions about expanding, changing, or stopping the project, and funders generally remember an applicant who reported a disappointing result honestly far longer than one who reported only successes.

Think about sustainability from the start. Identify who will pay for licenses or training when the grant ends, and what evidence will be needed to justify the cost. A project that has a clear path to continuation, or an honest plan for ending, is far more convincing than one that assumes funding will appear, because a pilot that quietly disappears when the money runs out teaches staff that new efforts are not worth their energy.