CATLR Tips and Resources

Iterative and Incremental Assignments for Deeper Learning

Help students improve their work before the final version is due

Overview

Students often submit a major assignment, get a grade, and only then learn what went wrong, when it is too late to make use of that learning and fix anything. By that point, feedback can’t change the work in front of them, and it rarely changes the next assignment either.

Iterative and incremental assignment design addresses this by breaking a large assignment into a sequence of smaller, low-stakes pieces. Students submit each piece, receive feedback, and use it before moving on. The final product grows out of work they have already revised.

Iterative means returning to the same piece of work to refine it. It may be drafting and redrafting an essay or it may be doing several shorter assignments that they weave together into a final product. Incremental means building the deliverable out part by part, where each new section depends on the last. A well-designed assignment often does both: students revise earlier pieces while adding new ones that build on them.

What makes this more than turning work in early is the feedback loop. A loop closes only when students act on what they receive (Sadler, 1989). When that happens, feedback becomes a form of teaching that guides the next step.

The approach can also make a course fairer. Students who have never produced a particular kind of work—often first-generation or multilingual students—benefit most when expectations become visible early, before one high-stakes deadline (Winkelmes et al., 2016). Staged deliverables surface those expectations and give every student a checkpoint along the way.

You don’t need to redesign a whole course to try this. Breaking a single major assignment into two or three feedback-informed stages is enough to begin, and starting small keeps the workload manageable while you learn what works in your own context.

In This Guide

Strategies for managing the feedback load

Responding at every milestone takes time, so design the sequence to keep that load sustainable.

  • Build in real dependencies. Make each deliverable require the previous one. This keeps interim steps from becoming box-checking and ensures that your feedback shapes the next stage (sometimes called “feed-forward”).
  • Ask students to close the loop. Have students attach a few sentences at each stage explaining how they used the previous round of feedback. This small step turns feedback from something students receive into something they act on and it shows you whether your comments are landing.
  • Treat feedback as teaching. A targeted comment at the right moment often reinforces the content students were previously exposed to but not necessarily ready to act on.
  • Keep early feedback light and timely. Feedback only helps if it returns before the next deliverable is due. Block time on your calendar for it, and keep interim comments concise and focused rather than exhaustive.
  • Be explicit about criteria. Tell students what each stage should accomplish and how you will assess it. Clear criteria help students judge their own work which is a capacity good feedback is meant to build (Nicol & Macfarlane-Dick, 2006; Carless & Boud, 2018).
  • Use low-stakes grading for interim work. Completion credit or simple pass/revise marks keep the focus on improvement rather than points.

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Supporting the process with generative AI

Generative AI can serve several purposes for this type of work. Faculty can use it to brainstorm and create different ways to break down their traditional assignments into iterative and incremental assignments. Along those lines, it can help in figuring out the evaluation structure in terms of what iterations should be graded, pass/fail, or peer-reviewed and what kind of points to award each stage. It can also help develop a rubric for each stage that builds on the previous approach. Finally, AI can help anticipate as well as build out a feedback bank for likely issues that are to arise with each stage and help with wording the feedback.

Generative AI might also be considered within this process for students. At an early stage, you can have one phase include sharing their work with AI to get feedback (and maybe a rubric score) based upon a prompt or Claude Project you give to them. After getting that feedback, their deliverable could include (1) their initial work, (2) feedback from the AI, and (3) their own response to that feedback in relation to their project goals. This can help students look to AI as support for their learning rather than a replacement.

For practical guidance on bringing these tools into your assignments, see CATLR’s Teaching with AI resources.

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Practice in Action

Picture an assignment that asks students to produce a short explainer video. Instead of collecting one finished video at the end of the term, you structure the work as a series of connected deliverables, each building on feedback from the last.

A sequence might look like this:

  • Interim 1 — Project plan. Students draft a plan describing their topic, audience, and approach. You respond with brief feedback on focus and feasibility.
  • Interim 2 — Revised plan plus outline. Students revise the plan using your feedback and draft an outline of the video’s content. You respond with brief feedback about the general shape of the outline.
  • Interim 3 — Revised outline plus storyboard. Students sharpen the outline and sketch a storyboard, mapping how the content will look and sound. You respond with brief feedback about both creative and practical considerations.
  • Interim 4 — Revised storyboard plus draft video. Students refine the storyboard and produce a rough cut. You respond with brief feedback about how the project is coming together and what it needs to go the last mile.
  • Final — Finished video. Students fold in the last round of feedback to produce a polished version.

Each stage carries low stakes on its own, so students can take risks and respond to feedback without much penalty. Because each deliverable depends on the one before it, the feedback has somewhere to go. Students can apply it right away to the next piece rather than filing it away. By the time the final video is due, students have already revised most of its underlying parts.

You’ll know the structure is working when students start referencing earlier feedback in later drafts, when fewer projects arrive in crisis at the deadline, and when revisions move past surface fixes toward real changes in thinking. Those signals also tell you where to adjust—if interim work looks rushed or feedback goes unused, a stage may need clearer criteria or firmer dependencies.

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References and Additional Reading

Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325. https://doi.org/10.1080/02602938.2018.1463354

Meyer, J., Jansen, T., Schiller, R., Liebenow, L. W., Steinbach, M., Horbach, A., & Fleckenstein, J. (2024). Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions. Computers and Education: Artificial Intelligence, 6, 100199. https://doi.org/10.1016/j.caeai.2024.100199

Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090

Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119–144. https://doi.org/10.1007/BF00117714

Winkelmes, M.-A., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., & Weavil, K. H. (2016). A teaching intervention that increases underserved college students’ success. Peer Review, 18(1/2), 31–36.

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