The Assignment Assistant

Students are already using AI around their coursework. The question is whether that use helps them think through the assignment — or helps them bypass the thinking the assignment was designed to develop.

Assignment Assistant gives students a better path. It turns an existing course assignment into a short, focused conversation with an AI coach built around that assignment’s learning goals, expectations, boundaries, and desired student output.

The instructor does not need to change the assignment or revise the syllabus. We work with the assignment as it already exists.

What students do

Before or during the assignment, students complete a brief coaching conversation. Depending on the assignment, the coach may help them clarify the task, form an initial idea, apply a course concept, compare examples, test a claim, prepare for discussion, or develop a short plan.

The student leaves with a useful preparation artifact: notes, an outline, a claim, a comparison, a project plan, or another form of structured thinking appropriate to the assignment.

The goal is not for AI to produce the final submission. The goal is to help the student get ready to do the work themselves.

What instructors get

Assignment Assistant gives instructors a way to guide AI use instead of simply worrying about it.

Rather than sending students from an assignment prompt into generic ChatGPT, the instructor can offer a bounded, course-specific AI environment that reflects the purpose of the assignment.

Depending on the assignment and instructor preference, the instructor may receive a completion record, student notes, selected excerpts, or a short summary showing how students approached the task.

The basic promise is simple:

Students get a better AI to use. Instructors get a better window into student thinking.

What kinds of assignments work well?

Assignment Assistant is best for assignments where students need to think something through before producing the final work.

Examples include:

• a paper where students need to develop a claim before drafting
• a case response where students need to weigh competing considerations
• a reflection where students need to connect experience to a course concept
• a recommendation memo where students need to justify a position
• a discussion-prep task where students need to arrive with something substantive to say
• a comparison assignment where students need to identify meaningful similarities and differences
• a project assignment where students need to clarify purpose, audience, or strategy

The Assistant does not replace the assignment. It creates a short guided step that helps students approach the assignment more thoughtfully.

Fall 2026 pilot

Scalable Learning has a few remaining slots for faculty who want to try Assignment Assistant with one existing assignment this fall.

Participation is deliberately light. You choose an assignment; we meet with you briefly to understand what kind of student thinking the assignment is meant to develop; then we build a short Assistant for students to use before or during the work.

No syllabus redesign is required. No assignment rewrite is required.

What participation involves

1. Select one assignment
   Choose an assignment where students would benefit from a short coaching conversation before completing the work.

2. Meet briefly with us
   We discuss the assignment’s purpose, where students tend to struggle, and what kind of thinking the Assistant should support.

3. Review the Assistant’s focus
   We provide a short description of how the Assistant will behave, what it will help students do, and where it will stop.

4. Try the prototype
   You test the Assistant briefly by pretending to be a student.

5. Launch with students
   We provide student access. No personally identifiable student information needs to be shared with Scalable Learning.

Why pilot?

A pilot gives you a low-effort way to explore how AI might improve one assignment you already teach.

It may help students start earlier, think more clearly, and use AI in a more purposeful way. It may also give you a clearer sense of how students are interpreting the assignment before they submit final work.

More broadly, it is a chance to help shape a practical model for AI-supported teaching: not prohibition, not generic chatbot use, but a designed learning environment built around the work students actually need to do.

For more information

Contact David Foster at Scalable Learning:
dfoster@scalablelearning.net