Ensuring Student Work Authenticity in an AI-Driven World
As educators work to navigate AI policies and approaches, one of the most important steps has been building AI literacy among professionals first—ensuring that those guiding students have the knowledge and confidence to integrate AI responsibly. While having strong policies in place is essential, much of what is surfacing in this conversation goes beyond rules and enforcement. Instead, it challenges us to examine the learning experiences we create for students. Are our lessons fostering deep engagement, social interaction, higher levels of Depth of Knowledge (DoK), and a sense of curiosity and wonder? Do students come to class eager to explore, ask questions, and engage in meaningful, high-level learning? Ultimately, the best way to address AI’s role in education isn’t just through detection tools or rigid policies—it’s by designing authentic, inquiry-driven experiences that make learning so compelling that students want to take ownership of their work.
1. The Swiss Cheese Model for Academic Integrity
The Swiss Cheese Model, introduced by Phillip Dawson, is a helpful framework for assessment integrity. Instead of relying on a single method, this approach layers multiple strategies to minimize gaps where AI-generated work could slip through.
Key Strategies in the Swiss Cheese Model:
Process-Based Assessment – Require students to document drafts, revisions, and reflections.
Oral Explanations & Video Reflections – Ask students to verbally explain their work or present their ideas in a short video.
AI Usage Transparency – Require students to document how they used AI, if applicable.
Alternative Assessments – Use real-world problem-solving, collaboration, and creative projects that are difficult to automate with AI.
Further Learning:
Podcast Episode: "Assessment and Swiss Cheese" – Phillip Dawson
Swiss Cheese Model Overview
2. Insights from Anna Mills on AI Detection and Process-Based Verification
While AI detection tools like Turnitin’s AI checker can help, they should not be the sole verification method. Anna Mills advocates for a balanced approach that combines structured writing processes, student reflections, and diversified assessment formats.
Key Strategies from Anna Mills:
Process Tracking: Require students to submit brainstorming notes, multiple drafts, and revisions to demonstrate their thought process.
Peer Review & Tutoring: Encourage students to engage with peer feedback cycles and writing centers.
Multimodal Assignments: Ask students to create audio or video reflections explaining their research and writing.
AI Transparency: Teach students to document and critique AI-assisted work rather than banning it outright.
Further Learning:
Anna Mills on AI Detection
Template for Critiquing AI Outputs
3. AI Validation and Critical Thinking in Assessments
A key literacy skill is learning how to evaluate AI-generated content critically. These resources provide practical ways to integrate AI validation lessons into the curriculum.
AI Pedagogy Project – AI Validation Lesson
Types of Feedback – Open Educational Resource (OER) Library
4. AI-Resilient Assignments and Alternative Approaches
Instead of trying to "AI-proof" assignments, educators should design assessments that require deep engagement and critical thinking.
30 Ideas for AI-Resilient Assignments
Critique of "AI-Proofing" Courses
5. Practical Strategies from Recent AI Workshops
In a recent AI in Education session with Kimberly Becker, we explored AI’s role in assessment, ethical considerations, and best practices for ensuring academic integrity.
Key Takeaways:
AI literacy is essential for both students and educators to navigate ethical and practical challenges.
AI tools should complement, not replace, critical thinking and writing processes.
Process tracking, social annotation, and peer review encourage students to engage deeply in their work.
Navigating ambiguity and fostering curiosity are key in an AI-driven world.
Further Learning:
Recording of the AI in Education Workshop
6. Next Steps for Educators
For teachers seeking guidance on AI in assessment, the following approaches can be valuable:
Set Clear AI Guidelines – Define when and how AI can be used in assignments.
Incorporate AI Literacy – Teach students how to navigate AI ethically and effectively.
Adopt Process-Based Assessments – Require student reflection, documentation, and revisions.
Explore Alternative Assessments – Use video reflections, discussions, and problem-solving tasks.
Would love to continue this conversation and provide additional resources if needed. Let me know what would be most helpful.
Additional Resources
Classroom Strategies & Frameworks
Depth of Knowledge (Webb, DoK) – Iowa ASCD
Universal Constructs for 21st Century Success
Cognitive Rigor and DoK – Karin Hess, PhD
AI, Plagiarism, and Ethical Use
AI Detection in Education is a Dead End
Stop Focusing on Plagiarism, Even Though ChatGPT Is Here | Harvard Business Review
How to Cite AI in Academic Writing – Moxie
Traffic Light System for identifying how you expect students to interact with AI for a given assignment:
Red Light: Define activities or tasks where AI usage is strictly prohibited, such as summative assessments or exams.
Yellow Light: Outline scenarios where AI can be used as a tool to aid learning, but with specific guidelines on how to properly cite and acknowledge the AI’s contributions.
Green Light: Identify opportunities where AI can be actively encouraged and integrated into the learning process, such as brainstorming, research, or content generation, with the expectation that students will reflect on and demonstrate their understanding.
Source: Aaron Maurer blog post
Create clear classroom policies
AI Course Policy Flowchart - Aaron Maurer
AI For Education Student Use Flowchart - Aaron Maurer
Assess Process over Product
Source: Aaron Maurer blog post
TRUST framework
Transparency
Real World Applications
Universal Design for Learning
Social Knowledge Construction
Trial and Error
If you find that a student is using AI inappropriately, how can you have that conversation?
Remember, you AND your students are caught in what I call the, “the ‘messy middle,’ a transitional space where traditional educational practices are being reexamined in the light of the world of artificial intelligence.”
Source: Aaron Maurer blog post
MORE RESOURCES ABOUT AI + CHEATING:
Moxie - Kimberly and Jessica on my podcast
Bloom's Taxonomy in the Age of Generative AI
Oregon State University has developed a resource to help faculty assess learning levels in their courses, considering the impact of Generative AI. This guide supports reflection on activities, assessments, and course outcomes.
Key Points:
Lower-Order Thinking Skills: Remembering, understanding, and applying are now easily augmented by AI tools.
Higher-Order Thinking Skills: Analyzing, evaluating, and creating remain crucial human skills.
Reassessing Course Design: Faculty should focus on developing higher-order skills that AI cannot replicate.
AI as a Tool: Incorporate AI into assignments to enhance learning rather than replace it.
Ethical Considerations: Address responsible AI use and academic integrity in course policies.
Recommendations:
Revise assessments to emphasize critical thinking and creativity
Design assignments that require human judgment and interpretation
Teach students to use AI as a learning aid, not a substitute for understanding
Develop AI literacy alongside subject-matter expertise
Acceptable Use of AI Framework
This framework, created by Vera Cubero, establishes a common understanding between students and teachers regarding AI use in assignments. It addresses both disclosure requirements and the extent of AI usage.
Key Components:
AI Usage Levels:
Level 0: No AI use allowed
Level 1: Limited AI use for brainstorming
Level 2: Moderate AI use for research and drafting
Level 3: Extensive AI use, with human refinement
Level 4: AI-generated content with human oversight
Disclosure Requirements:
Clear guidelines on how students should report AI usage
Varying levels of detail required based on the extent of AI use
Pedagogical Considerations:
Alignment of AI usage with learning outcomes
Emphasis on critical thinking and original contribution
Ethical Integration:
Promotes responsible AI use in academic settings
Balances innovation with academic integrity
Implementation:
Instructors specify the acceptable AI level for each assignment
Students adhere to the specified level and disclosure requirements
Promotes transparency and fairness in assessment
This framework, adapted from Dr. Mike Perkins' work based on Dr. Leon Furze's research, offers a structured approach to integrating AI in education while maintaining academic standards
AI Use Guide Helps Students Navigate AI in Learning
Elon University and AAC&U released a free guide, AI-U/v1.0, to help college students navigate AI's role in education, offering practical advice on using AI responsibly and ethically.
The guide provides students with AI usage ground rules, ethical checklists, career preparation tips, and resources for using AI effectively in an academic setting.
The initiative, which involved contributions from over 100 students and faculty from 14 countries, is part of Elon University’s ongoing efforts to prepare students for the AI revolution, with plans for regular updates.