Assignment Examples with AI Guidelines
Classroom-tested activities organized by discipline and AI permission level, with implementation guidance you can adapt.
Assignment Examples with AI Guidelines
Classroom-tested assignment activities organized by discipline and AI permission level. Each includes implementation guidance you can adapt for your courses.
Humanities
AI Encouraged with Documentation
Creative Writing with AI as Partner
Students create short stories using AI-generated prompts or characters to add unique elements to their narratives. Learning objective: Explore how AI tools can be used as creative partners in the storytelling process.
Implementation: Students document which AI-generated elements they used and reflect on how the AI partnership influenced their creative decisions.
Visual Storytelling with AI Image Generation
In literature or art courses, students use AI image generation (Adobe Firefly, DALL-E) to visualize characters from literature or generate visual interpretations of themes.
Implementation: Students include artist statements explaining their prompts, the images generated, and how the visual interpretations enhance understanding of the text.
AI Limited Use (Specific Stages Only)
Metacognitive Writing Series
Across multiple short essays, students use AI for one specific task in Assignment 1 (e.g., outlining), then reverse the approach in Assignment 2 (student writes outline, AI writes draft). Each assignment includes a cover sheet reflecting on the experience.
Reflection prompts: Did AI make writing easier? Did you learn more or less? Does the result reflect your skills? Would this use be ethical without explicit permission?
AI-Assisted Revision Practice
Give students an AI-generated essay response to your prompt and ask them to revise it (with explicit caveat they cannot abandon the draft and start over). Students discuss or write about their revision process.
Discussion points: What did you keep vs. change? Were there fundamental flaws you couldn’t fix? What does this teach you about AI’s understanding of academic writing?
AI for Critical Analysis (No AI in Student Production)
Critical Evaluation of AI Explanations
Ask AI to explain a central course concept or summarize a major reading. Students evaluate the results individually, in small groups, or as a class.
Evaluation criteria: Was it accurate? Did it make up details or distort ideas? What major ideas did it omit? Does it include fictional sources or misinformation? What was the AI’s overall approach to summarizing?
Coaching AI to Revise Its Own Work
Students receive an AI-generated essay and must coach the AI to revise according to their specifications (e.g., “make the thesis more specific,” “engage sources more thoroughly”).
Learning goals: Generate constructive feedback (transferable skill) and discover the limits of AI’s revision capabilities. At what point can AI not improve its own work in beneficial ways?
STEM & Quantitative Fields
AI Encouraged with Documentation
Preliminary Data Analysis Verification
Students use AI to conduct preliminary analysis of datasets to confirm broad takeaways, then perform their own more nuanced analysis to build on these foundations.
Disclosure requirements: Describe AI tool used, types of prompts provided (conceptual questions, code examples, feedback requests), identify which portions include AI-generated content, explain modifications made to AI output.
Code Development with AI Assistance
Students may use AI for requesting code examples, debugging assistance, or feedback on their code, but must develop skills to critically evaluate AI-generated solutions.
Critical requirement: Students must explain their understanding of how the code works and be able to identify when AI produces incorrect results. Final work must reflect their own understanding and analysis.
Cross-Disciplinary Applications
Assignment Prompt Analysis
Students input assignment prompt into AI one sentence at a time, observing how results improve with added detail. Applicable across all disciplines to teach students to understand assignment requirements.
Discussion: What are the key details of the prompt? How does AI interpretation change with more context? What does this teach us about effective prompting and assignment comprehension?
Peer Review Practice with AI-Generated Work
Students practice peer review using an AI-generated response alongside a student-written draft. Since AI writing is often polished but shallow while human drafts have rough edges but deeper ideas, students learn to provide different types of constructive feedback.
Application: Valuable for any discipline requiring peer review, revision, or collaborative feedback processes.
Student AI Use Disclosure Templates
When students are permitted to use AI, these templates help them document their use transparently:
Basic Acknowledgment Statement
“I acknowledge the use of [AI Tool Name] (link to tool) to [specific use]. The prompts used include [list]. The output was used to [explain specific use]. I reviewed and edited all content to ensure accuracy and originality.”
Detailed Reflection Template (150-300 words) should include:
- Citation for tool(s) used
- Explanation of why you decided to use the tool
- Description of how you used it to manage assignment requirements
- Reflection on experience: what worked, what didn’t, limitations, potential biases
Sources: Princeton University, Newman University, Kansas State University, Notre Dame
Resources for Assignment Design
Stanford Teaching Commons – Integrating AI into Assignments
teachingcommons.stanford.edu →Cornell CTI – AI in Assignment Design
teaching.cornell.edu →NC State – Designing Assignments with AI in Mind
teaching-resources.delta.ncsu.edu →Carleton College – AI-Based Assignments and Activities
carleton.edu →Compiled by the Faculty Development for Student Success Center | Jackson State University | www.jsums.edu/scholars
Social Sciences
AI Encouraged with Documentation
Community Problem-Solving with AI Data Analysis
Activity Type: Applied Research | Source: ACUE Best Practices
Students collaborate to develop an AI-driven solution for a community issue, analyzing datasets related to local challenges, discussing ethical considerations, and designing an application.
Documentation required: Description of AI use in data analysis, ethical considerations discussed, acknowledgment of any AI-generated insights or visualizations.
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Marketing Campaign with AI Audience Analysis
Activity Type: Strategic Planning | Source: ACUE Best Practices
Students create marketing campaigns using AI-generated insights to refine target audience, messaging, and content strategy. AI is positioned as a strategic partner, not a replacement for critical thinking.
Required reflection: How did AI insights influence your strategy? What AI-generated recommendations did you reject and why? How does this amplify rather than replace your strategic thinking?
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AI Limited Use (Specific Stages Only)
Analyzing Bias in AI Algorithms
Activity Type: Ethical Analysis | Source: ACUE Best Practices
Class discussion examining real-world cases where biased algorithms perpetuated inequalities. Students explore how biased training data impacts AI decision-making and societal implications.
Implementation: Students may use AI to help organize research on these cases, but analysis and conclusions must be their own. Emphasis on developing critical thinking about seemingly neutral technologies.
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