Crafting Assignments to Address Specific AI Concerns

Examples of AI-inclusive assignment strategies you might take with your class assignments and assessments include:

• Comparative Analysis: Have students compare AI-generated responses with expert sources or their own analysis
• AI for Brainstorming: Students use AI for initial brainstorming or drafting, then critique and substantially revise, documenting AI prompts used, revisions made (and why), and questions or concerns that are raised along the way
• Error Detection: Provide students with your own AI-generated content that includes intentional flaws for students to identify and correct
• Prompt Engineering: Assign tasks where students develop and refine prompts to achieve specific outcomes
• AI Tool Evaluation: Have students test multiple AI tools and compare their effectiveness for specific tasks

We recommend always requiring reflection on the AI collaboration process. Ask students to document what AI helped with, where it fell short, and what unique human contributions they made. This metacognitive process is crucial for deep learning.

Example assignment: “Use AI to generate three different approaches to solving [insert problem you want students to evaluate]. Evaluate each approach, identify its limitations, and develop your own synthesis that improves upon the AI suggestions. Write a reflection explaining your decision-making process.”

When designing assignments and assessments to be AI-resistant, focus on process, personalization, and synthesis rather than just final products. Require multiple drafts, reflective essays, or connections to specific course discussions.

Use local examples, current events, or personal experiences. Design assignments that benefit from AI collaboration rather than fighting it—for instance, having students critique and improve AI-generated responses.

AI-resistant strategies include incorporating:

• Process-focused assessments
• Personalized and contextual tasks
• Authentic performance tasks
• In-class and synchronous assessments
• Metacognitive and reflective approaches
• Assessment design principles

The most effective AI-resistant assessments might combine several strategies, creating multi-faceted learning experiences that would be difficult for AI to complete meaningfully while genuinely supporting student learning objectives. Prior to reviewing these specific strategies, please also consider:

• Learning Enhancement: Any chosen strategy should enhance learning rather than simply make assignments harder. The goal is meaningful engagement with content.
• Accessibility: Ensure AI-resistant elements don’t inadvertently disadvantage students with disabilities or different learning styles.
• Scalability: Consider grading time and complexity – some approaches work better for smaller classes.
• Learning Objectives Alignment: The resistance strategies should serve pedagogical goals, not simply prevent AI use.
• Student Communication: Clearly explain why these approaches benefit learning, not just why AI is problematic.

When discussing AI, it’s important to recognize that the term encompasses a wide range of technologies, some of which may not align specifically with what you’re referring to. Below is a breakdown of how AI appears in some of our academic software:

Many tools have incorporated AI for years. For example, Mathematica and MATLAB offer machine learning toolboxes, ESRI products include deep learning models for pattern recognition and predictive analysis. These forms of AI focus on structured modeling and analysis and are distinct from the generative AI currently receiving the most attention.

Some platforms, like Mathematica, offer a sophisticated mix of AI options, including the integration of generative AI models through API connections, code assistance, machine learning modules, etc.

You may want to start by identifying where AI is already being used in your field professionally. In business, that might be data analysis; in writing, it could be editing assistance; in sciences, perhaps literature reviews.

We recommend introducing tools gradually and always connecting AI use to learning objectives and professional development. Consider how professionals in your field are using AI and prepare students for those realities.

Some discipline-specific questions you might have related to AI might be:

• STEM: “Can AI solve problems better than students should learn to?” Students still need to understand underlying principles to verify AI solutions, catch errors, and apply knowledge to novel situations. Use AI to handle calculations while focusing on problem setup, interpretation, and application. Teach students to be sophisticated users who can spot AI mistakes.
• Humanities: “Is AI killing creative writing and critical thinking?” AI can generate text, but it can’t replicate authentic personal voice, lived experience, or nuanced cultural analysis. Use AI as a starting point for student critique and improvement. Focus on developing unique perspectives and sophisticated analysis that goes beyond AI capabilities.
• Professional Programs: “How is AI changing my field’s professional standards?” Research current professional discussions about AI in your field. Invite practitioners to discuss real-world AI use. Teach students both technical skills and ethical frameworks they’ll need as professionals. Prepare them to be leaders in establishing good AI practices.

We continue to see more generative AI embedded directly in software. It can show up as assistants, automated categorization, or content generators. In some cases, these features can be enabled or disabled by local administrators. In others, generative AI is integrated by default, with limited or no ability to turn it off. If you have questions about AI in centrally-purchased tools related to your discipline, please reach out to DLINQ for a conversation.

Staged Writing Assignments

• Require submission of outlines, rough drafts, peer feedback, and revision logs
• Include reflection essays explaining writing choices and revision decisions
• Example: “Submit your thesis statement and three supporting arguments by Week 3, first draft by Week 5, peer review comments by Week 6, and final paper with a 500-word reflection on your revision process”

Show-Your-Work Mathematics/Sciences

• Require detailed step-by-step solutions with explanations
• Ask students to identify and correct common mistakes
• Example: “Solve this calculus problem and explain each step. Then identify where a typical student might make errors and explain why those approaches are incorrect”

Research Process Documentation

• Require annotated bibliographies with evaluation criteria
• Ask for search strategy explanations and source comparison
• Example: “Document your research process: include 3 initial search terms, 2 database searches that failed, explanation of how you refined your approach, and evaluation of why you selected your final 5 sources over others you considered”

Local/Institutional Context

• Reference specific campus locations, local businesses, or regional issues
• Use institutional data or case studies
• Example: “Analyze the sustainability initiatives at our university’s dining halls and propose three improvements based on your observations from eating there this semester”

Student Experience Integration

• Connect to internships, jobs, or personal experiences
• Require specific examples from their lives
• Example: “Drawing on your recent internship experience, identify a workplace communication challenge you observed and apply three course theories to explain and address it”

Current Events with Specific Angles

• Use very recent news (within days) with discipline-specific analysis
• Require synthesis with course materials
• Example: “Analyze yesterday’s Federal Reserve announcement using the economic principles from Chapters 12-14, incorporating at least two specific examples from our class discussions this week”

• Professional Simulations
• Role-play scenarios requiring real-time decision making
• Client consultations or presentations
• Example: “You’re presenting to our ‘client’ (played by a peer) who will interrupt with questions. Prepare a 10-minute marketing strategy presentation and be ready to defend your choices and adapt on the spot”

• Open-ended challenges with multiple viable solutions
• Innovation and design thinking exercises
• Example: “Design an intervention to reduce food waste in dormitories using only resources available on our campus and a budget of $500. Present three different approaches and explain your final recommendation”

Live Collaboration

• Group problem-solving with rotating roles
• Peer teaching exercises
• Example: “In rotating pairs, each student teaches a concept to their partner for 5 minutes, then answers questions. Partners evaluate the teaching effectiveness”

Oral Examinations

• Conversational assessments with follow-up questions
• Defense of written work with probing inquiries
• Example: “Explain your research findings, then respond to three challenging questions about methodology, alternative interpretations, and implications”

Hands-On Demonstrations

• Lab skills, technical procedures, or physical tasks
• Real-time problem-solving with equipment or materials
• Example: “Demonstrate three different microscopy techniques and explain when each would be most appropriate, adjusting for the specific sample I provide”

Learning Process Analysis

• Detailed reflection on how understanding evolved
• Error analysis and correction strategies
• Example: “Identify the three most challenging concepts from this unit, explain why they were difficult, describe what strategies helped you understand them, and create a study guide for a peer who’s struggling with the same concepts”

AI Collaboration Documentation

• When AI use is permitted, require detailed logs of interaction
• Critical evaluation of AI outputs
• Example: “If you use AI tools, submit a log of your prompts, evaluate the quality of responses, identify errors or limitations, and explain how you verified or improved the AI’s contributions”

Multi-Modal Requirements

• Combine written, visual, oral, and practical components
• Make it difficult to rely on single AI outputs
• Example: A project requiring a research paper, infographic, 3-minute video explanation, and live Q&A session

Time-Bound Elements

• Include components that must be completed quickly
• Use synchronous discussions or rapid-response formats
• Example: “Submit your initial analysis within 2 hours of receiving the case study, then participate in a live discussion forum that evening”

Collaborative Verification

• Peer review and validation processes
• Group accountability measures
• Example: “Each team member must verify and sign off on specific sections, and teams will present together with individual questioning”

Teach ethical AI use as a skill, not just a rule. Help students develop judgment about when and how to use AI appropriately.

Ethical Decision-Making Framework

The “Three Questions” Approach:

1. What am I supposed to be learning? If AI does the work, am I bypassing the learning objective?
2. Am I being transparent? Would I be comfortable explaining my AI use to my instructor?
3. Could I do this without AI? Do I understand the content well enough to verify and improve AI outputs?

Teaching Strategies

• Use case studies: Present scenarios and have students discuss whether AI use would be appropriate
• Create gray area discussions: Not everything is black and white; help students navigate ambiguity
• Model ethical reasoning: Share your own decision-making process when you use AI tools
• Discuss professional contexts: Connect to how they’ll need to make these decisions in their careers
• Practice attribution: Teach students how to properly cite and acknowledge AI assistance

Help students understand that ethical AI use isn’t about avoiding technology—it’s about ensuring they’re still learning and growing as thinkers.

If your course learning objectives require students to work without AI assistance, here are some strategies you might use to discourage students from using AI.

Clear Policy Communication

If you’re prohibiting AI use, be explicit and explain why. For example: “For this assignment, you may not use AI tools because the goal is to develop your ability to [specific skill]. This skill is fundamental to [professional/academic context] and requires independent practice.”

Prevention Strategies

• AI-Resistant Assessments: Review this page’s suggestions for AI-resistant assessments
• In-Class Assessments: Exams, quizzes, and writing tasks completed in supervised settings
• Oral Examinations: Have students present or defend their work verbally
• Handwritten Submissions: Require handwritten work for certain assignments
• Time-Bound Tasks: Short completion windows that make AI consultation impractical
• Progressive Assignments: Require multiple checkpoints (outlines, drafts, revisions) that show development over time

Important Considerations

• Recognize that complete prohibition is difficult to enforce for take-home work
• Consider whether the prohibition serves learning objectives or is simply about control or technology fears
• Be prepared to explain your reasoning if students question the policy
• Ensure your restrictions don’t inadvertently disadvantage students or generate equity concerns
• Consider how your own restrictions would impact you if you were asked to comply with them for your own research or proposal writing (e.g., being asked to handwrite your submissions or submit to an oral examination about them)

Alternative Approaches

Instead of blanket bans, consider “AI-resistant” assignments. Design tasks where AI assistance is limited or obvious, such as those requiring personal experience, local context, or process documentation. See our section on “Bringing AI Meaningfully into My Class” and “Setting Clear Expectations” for more ideas related to this strategy.

Remember that the professional world increasingly uses AI, so completely barring students from learning to work with these tools may not serve their long-term interests. Balance is key.

We recommend you try to address AI-related cheating concerns through a combination of clear policies, assignment redesign, and building a culture of integrity rather than relying solely on detection.

Multi-Pronged Approach

• Clear Communication: Be explicit about what constitutes appropriate vs. inappropriate AI use for your class as a whole, and for each assignment
• Assignment/Assessment Design: Create tasks that are difficult to complete meaningfully with AI alone (e.g., using process documentation, personal reflection, oral components such as class presentations and discussions, etc.)
• Early Conversations: Discuss academic integrity and AI on day one of class; explain the “why” behind your policies
• Low-Stakes Practice: If you are allowing AI use in your class, give students opportunities to practice appropriate AI use in assignments where mistakes aren’t costly, to start
• Assessment Variety: Use multiple assessment types so no single assignment carries too much weight

AI Detection Software

Generative AI detection tools are unreliable/flawed and are not considered valid proof of whether or not a student used a generative AI tool to complete an assignment. These tools are biased against students for whom English is a second language, and they can falsely flag students’ work as AI generated. We recommend that you avoid relying on these tools.

If You Suspect Inappropriate AI Use

If you suspect inappropriate AI use, we recommend you have a conversation with the student first. Ask students to explain their work, walk through their process, or elaborate on specific points or word choices. For example, ask open-ended questions such as “Tell me about your writing process for this section” or “How did you arrive at this particular argument?” This approach is educational and gives students a chance to demonstrate comprehension of a topic.

Remember also that students may not always understand what counts as “cheating” with AI. Many genuinely don’t know where the line is, making education more effective than punishment.

Here are some resources that dive deeper into process-focused learning and assessment:

• Process-focused Learning and Assessment for AI and Learning Skills
• Problems with “Process Over Product”
• Final Project: Our Process-Folios