Overview
As generative AI tools like ChatGPT become more widely available, many instructors are reflecting on how to approach assignment design in a way that aligns with their course goals and pedagogical values. Not everyone needs to overhaul their assignments, nor is that always necessary. But if you're thinking about redesigning some of your current assignments, this section offers practical ideas and strategies. Whether your goal is to minimize inappropriate use, engage students in critical dialogue about GenAI, or invite exploratory use of these tools, thoughtful assignment design can help.
Step 1: Review Your Learning Objectives
As with any teaching decision, it helps to start with your course learning goals. What do you want students to understand or be able to do by the end of your class? Are you aiming for knowledge recall, critical evaluation, creative synthesis, or disciplinary argumentation? How do your assignments currently support those goals? Once you’ve clarified your intended outcomes, it becomes easier to determine whether and how GenAI might disrupt, support, or reshape students’ learning along the way.
From there, you might ask:
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Is the process of writing or idea development central to the learning you want students to do?
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Are there opportunities where students might use GenAI to support learning, rather than shortcut it?
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Where might you need to increase transparency about your expectations?
Depending on your answers, it may make sense to reframe assignments around higher-order cognitive skills that GenAI tools cannot easily replicate. For instance, revisiting Bloom’s Taxonomy in light of AI reveals that while tasks like “Remember” and “Understand” can be automated, meaningful learning still resides in levels like “Evaluate” and “Create”. When your goals center on analyzing, critiquing, or crafting new arguments, students must engage generatively, not outsource their thinking.
Step 2: Choose a GenAI Design Approach That Aligns with Your Goals
Once your learning goals are clear, consider how you want GenAI to function or not function in your course. Will you adopt a resistant stance, allow selective or guided use, or integrate GenAI as a co-creator in the learning process?
This aligns with flexible frameworks like the AI Assessment Scale (AIAS). This model helps instructors articulate what levels of GenAI use are permitted (e.g., not at all, only in planning stages, or throughout the assignment) and what types of transparency, such as prompt disclosure or student reflection, are expected.
Depending on your goals and values, your assignment design might include strategies like:
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Requiring in-class or AI-free drafts followed by revision or reflection
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Allowing GenAI tools during brainstorming, but not during final drafting
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Using GenAI outputs as objects of critique or comparison
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Giving students the choice to use or avoid AI tools, with reflective documentation
Each approach reflects a set of teaching values and pedagogical priorities, but it’s important to also consider how these decisions affect student access and equity. For instance, some resistant practices (e.g., handwritten exams or timed oral presentations) may unintentionally disadvantage students who rely on assistive technologies, need extended time, or process language differently. Students with disabilities or non-native English speakers may find themselves at a disadvantage if support structures are not maintained alongside AI restrictions.
Step 3: Structure Assignments to Reflect Your Values and Goals
With your learning objectives clarified and your GenAI approach defined, the next step is to ensure your assignments support the kind of learning you value. The format you choose, whether it’s an essay, project, or presentation, should align with your course goals, your stance on GenAI use, and the specific skills you want students to develop. Just as important as the final product is the process students take to get there: how they generate ideas, revise their thinking, and reflect on their choices. In a GenAI context, structuring assignments to highlight this process can promote transparency and ensure that learning–not just task completion–remains central.
Depending on your goals, you might consider these strategies:
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Scaffold assignments: One powerful way to reinforce authentic learning is by making the process visible. Scaffolding assignments with stages like brainstorming, outlining, drafting, peer feedback, and reflection encourages students to develop their ideas gradually, rather than relying on GenAI to produce a polished result all at once. These stages not only support deeper engagement but also give instructors insight into how students are thinking and where AI may or may not have played a role.
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Incorporate personal or course-specific relevance: Assignments that ask students to engage deeply with course-specific materials also tend to resist superficial AI use. For example, tasks that require analysis of a particular lecture, image, video clip, or in-class example push students to draw on knowledge that GenAI tools don’t have access to. Similarly, prompts that invite students to incorporate personal experience, local context, or interdisciplinary perspectives encourage unique responses that can’t easily be outsourced.
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Introduce GenAI as an object of critique: If your goals allow for guided GenAI use, consider designing assignments where students analyze AI-generated content. For example, you might ask them to input your prompt into ChatGPT, critique its output, and reflect on its limitations. Or have students generate multiple outputs by varying their prompts and compare how small changes affect results. These activities foster digital literacy, sharpen students' evaluative skills, and help them understand the boundaries of what GenAI can and cannot do.
The most effective assignments might not necessarily be “AI-proof”, they are AI-aware, and designed to make thinking visible rather than merely testing content knowledge.
Step 4: Rethink Assessment Criteria and Feedback Loops
Once your assignments are designed to foster meaningful thinking and transparency, the next step is to revisit how you assess student work. In a GenAI-aware classroom, assessment isn't just about what students submit, it's about how they got there, what decisions they made, and how well they can articulate their thinking along the way. This means shifting emphasis from final outputs to the reasoning and learning processes behind them. What does it look like for students to demonstrate understanding, critical thinking, or creative synthesis in your course?
Rather than grading solely on final products, consider assessment strategies that:
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Highlight students' reasoning, decision-making, and development over time
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Include checkpoints that make the learning process visible (e.g., draft + reflection + revision)
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Provide opportunities for timely, formative feedback
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Align your rubrics with cognitive and metacognitive goals
When students know they’ll be evaluated on their process–not just the end result–they are more likely to take ownership of their work. This might involve assessing outlines, research logs, self-assessments, or reflections alongside the final product. You might consider asking students:
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What were their key decisions as they developed their response?
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How did their ideas change from first draft to final submission?
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Did they use any tools (including GenAI) in the process, and how did those tools shape their thinking?
To support instructors in this effort, our colleagues at the Center for Teaching & Learning have developed two rubrics to help instructors assess student thinking in ways that align with GenAI-aware course design. Adapted from national AAC&U frameworks, these Critical Thinking and Information Literacy rubrics emphasize students’ ability to evaluate evidence, synthesize ideas, and construct reasoned arguments, all skills that are central to deep learning and difficult for GenAI tools to replicate.
These rubrics are particularly useful when the goal is to assess how students are thinking, not just what they’ve produced. By making reasoning and reflection visible, the rubrics support a more transparent and equitable approach to assessment, one that rewards the learning process as much as the final product.