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Designing AI-Aware Assignments

A resource for Rutgers University Instructors

Generative AI (GenAI) can now complete many of the tasks you’ve traditionally asked students to do on their own, drafting essays, solving problem sets, summarizing readings, writing code, often in seconds. If you’ve found yourself wondering whether an assignment you’ve relied on for years still accomplishes what you wanted it to do, you’re not alone. The assignment hasn’t changed, but students now have tools that can bypass some of the thinking it was designed to build.

This resource starts with assignment design, not AI tools or policies. It walks you through a step-by-step method for deciding what role, if any, GenAI should play in each task you assign, grounded in your learning outcomes, Maha Bali’s cake-making analogy, and the AI Assessment Scale (Perkins et al., 2024) for communicating that decision to students. The resource then closes with guardrails and Rutgers resources to support your work. Whatever you decide, an AI-aware assignment is one where the choice was intentional and connected to student learning (Vee et al., 2026).

A few things you’ll find inside

  • A four-step method for deciding GenAI’s role in your assignments, task by task
  • Maha Bali’s cake-making analogy and the AI Assessment Scale, used together to decide and communicate GenAI’s role
  • A full worked example: redesigning a literature review
  • Guardrails for using GenAI responsibly, and where to go for Rutgers support

Click below to explore the full resource.

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References

Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(6), 49-66. https://doi.org/10.53761/q3azde36

Vee, A., Watkins, M., & Bruff, D. (2026). The Norton Guide to AI-Aware Teaching. W. W. Norton. https://wwnorton.com/books/9781324127673