Writing-intensive courses require students to produce and revise a substantial amount of text, and professors who assign Waterlily in such courses must manage a significant grading load. Class sizes may range from twenty to over a hundred, and each student may submit several drafts. Without a clear workflow, feedback becomes inconsistent and burnout becomes a real risk.

A good workflow starts with deciding what kind of feedback is needed at each stage. Early drafts benefit from comments on argument and organization, while final drafts require evaluation against the full rubric. Treating every stage the same wastes effort and overwhelms students.

The aim is to match feedback intensity to the purpose of the assignment. Low-stakes drafts can receive brief, focused comments, while high-stakes final papers warrant more detailed evaluation. Communicating this structure to students helps them understand how to use each piece of feedback.

Structure the Drafting Cycle

A three-stage cycle works well for many courses: a proposal or thesis statement, a full draft, and a revised final. Each stage has a specific focus, such as claim clarity in the first, evidence and structure in the second, and polish in the third. This sequence lets students build the essay gradually and gives you manageable chunks of work to review.

• Stage one: a thesis and outline reviewed for argument and scope
• Stage two: a full draft reviewed for evidence, analysis, and organization
• Stage three: a revised paper graded with the complete rubric
• A short reflection in which students explain how they responded to feedback
• Optional office hours or conferences for students who need additional support

Differentiate Feedback by Stage

At the proposal stage, limit comments to the thesis and plan, since polishing sentences too early is premature. At the draft stage, focus on how evidence supports claims and whether paragraphs build an argument. Save line-level editing for the final stage, when students are ready to benefit from it.

Consider using audio or video comments for some drafts, which can convey nuance faster than typing. Many professors find they can say in two minutes what would take ten to write. Students often report that spoken feedback feels more personal and easier to understand.

Handle Large Enrollments

In large sections, teaching assistants may share the grading load, which raises consistency concerns. Hold a norming session at the start of each assignment and provide a comment bank tied to the rubric. Spot-check a sample of each assistant's work to ensure standards remain aligned.

Some programs use rubric-based AI feedback tools such as GraideMind to generate first-draft comments on early submissions, which instructors then review and personalize. This can give students faster feedback between stages. It also frees instructors to devote more attention to interpretive discussion in conferences and final evaluations.

Keep Records and Close the Loop

Maintain a simple spreadsheet tracking each student's progress across stages, noting recurring issues and improvements. This record helps you identify who needs additional support and provides evidence for end-of-term evaluations. It also makes it easier to write letters of recommendation grounded in specific observations.

At the end of the term, review the workflow itself. Which stages produced the most improvement, and which consumed time without clear benefit? Adjusting the structure each semester gradually produces a system that is both effective and sustainable.