There is no reliable way to prevent the use of AI on assignments and no reliable way to detect the use of AI on assignments. Instructors must work together with students to build a culture of trust and authentic engagement. Effective assignment design and transparent policies help to support that culture. Academic integrity experts suggest that the best solution is to make the right thing easier to do, instead of making the wrong thing harder to do.
Though ChatGPT has certainly contributed to academic dishonesty, increased cheating has been reported in colleges and universities since before GenAI hit the popular market. Increased cheating is often related to changing student behaviors, circumstances, and values, not just changing tools.
Some self-reported reasons that students cheat include:
Assignments feel irrelevant
They face time pressures or conflicting priorities (work, family responsibilities, etc.)
They lack clear understanding of expectations
Education feels purely transactional
Classes are perceived as "busy work"
Performance pressure (from family, minimum GPA requirements, etc)
Focus on grades
“High stakes” exams or assignments, often defined by high point value
Judging course workload as too high
Feeling “anonymous,” disconnected from course material, a class community, professor, or institution
Peer acceptance of cheating
Perception that academic dishonesty will go unpunished
Misunderstanding plagiarism or how to avoid plagiarism
Cultural or regional differences about what constitutes plagiarism or academic dishonesty
Rather than relying on unreliable detection tools, institutions should focus on creating meaningful assignments, building supportive learning environments, and helping students understand appropriate uses of AI technology.
Like any set of values, values related to academic integrity are learned and developed over time. Many students arrive on campus without clear expectations, and this includes their understanding of college-level academic integrity. Students need their instructors to:
Explain what academic integrity is in the context of the course, the discipline, and the college.
Explain why these academic integrity rules exist (e.g. why we cite sources, why we work collaboratively or individually, how, why, and when we use co-authoring tools like ChatGPT or Grammarly).
Provide examples of what appropriate attribution and collaboration look like
Create opportunities for students to practice their learning and skills low-stakes assignments before major projects.
Demonstrate the importance of academic work by recognizing academic labor and processes as valuable learning experiences
Help students understand cultural differences by acknowledging that academic integrity norms may vary across cultures and disciplines.
Help students understand that academic labor and process are valuable learning experiences. For example:
In math, working through problem-solving steps builds critical thinking, even if AI could provide the answer
In science, conducting and documenting experiments develops observation skills and scientific reasoning, beyond just getting results
In writing, drafting and revision strengthen analytical and communication skills that AI can't replace
Academic integrity values are learned through consistent exposure, clear explanation, and meaningful practice. Students develop these values gradually as they understand their importance and see them modeled in their academic community. Regular discussions about academic integrity, combined with opportunities to practice ethical decision-making in low-stakes situations, help students internalize these values and apply them confidently throughout their academic careers. This ongoing process of learning and reinforcement is especially important as students navigate new tools like AI and adapt to evolving academic expectations.
While departmental statements outline broad AI policies, instructors should provide clear, assignment-specific guidelines about AI use. These guidelines help students make ethical decisions and understand how departmental policies apply to specific tasks. They also demonstrate to students why limiting or enabling AI use is important and reiterate how their work on assignments contributes to their learning.
When developing assignment-specific AI guidelines, consider using this scale to clearly communicate acceptable levels of AI engagement:
Level 1: Independent Work: Complete tasks without any AI assistance to show case individual skills and knowledge.
Level 2: AI-Assisted Ideation and Refinement: Use AI to generate ideas and improve the quality of your work (e.g., brainstorming, refining structure). The core content remains your own.
Level 3: AI-Assisted Drafting: Create initial drafts with AI, but critically evaluate and revise them. This involves modifying AI output to ensure originality.
Level 4: AI Collaboration: Work collaboratively with AI on tasks, maintaining ownership while critically evaluating AI content.
When creating assignment-specific AI guidelines, include these key elements:
AI Permission & Justification: Clearly state which level of AI use is permitted and explain why this level is appropriate for the learning objectives. Even when AI use is not permitted, explain the pedagogical reasoning to help students understand the connection between assignment design and learning objectives.
Use Parameters: Provide specific examples of acceptable and unacceptable AI use for the assignment.
Platform Recommendations: If applicable, suggest specific AI tools that are appropriate for the permitted uses.
Attribution Requirements: Explain how students should document their AI use in their work.
Instructor AI Use: Be transparent about how you, as the instructor, use AI in creating or evaluating assignments.
Students need reminders about AI use for a couple of reasons:
AI guidelines often vary by discipline and course. In one course, it may be acceptable to engage in Level 4 AI use on most assignments, while others may limit use to Level 1.
Clear statements about the value of assignments help students understand why independent work matters for their learning. When students see the connection between assignment design and learning outcomes, they're more likely to engage authentically with the work.
Regular reminders normalize discussions about AI use and create opportunities for students to ask questions about appropriate AI engagement. Clear guidelines help students develop good habits around AI use that will serve them in their academic and professional careers.
The Hub offers comprehensive resources to help both faculty and students navigate academic integrity in the digital age. Faculty can access support for designing assignments that promote authentic learning, while students can get assistance with research, citation, and proper use of academic tools. With locations at both CentreTech and Lowry campuses, plus online services, The Hub provides tutoring, library resources, and technology assistance to help build strong academic integrity practices.
Research Appointments for Students (practicing finding and citing research)
Professional Development for Faculty & Instructors
Effective Strategies for Promoting Academic Integrity:
Create supportive learning environments that build trust
Design meaningful assignments connected to students' goals
Integrate AI responsibly with clear guidelines
Focus on process documentation
Address systemic issues that drive cheating
Update academic integrity policies to specifically address AI
The TRUST Model for Assignment Design was developed to help reduce instances of cheating and plagiarism by directly addressing the reasons students do it.
Transparency: Clear communication about assignment purpose and requirements
Real World Applications: Making assignments relevant to real-world scenarios
Universal Design for Learning: Reducing barriers and increasing access. To learn more about this framework, read UDL: A Powerful Framework and explore the UDL on Campus website and check out the CCA UDL Summit on March 28.
Social Knowledge Construction: Incorporating peer interaction and feedback
Trial and Error: Allowing students to learn from mistakes through revision
Experts have argued that banning AI won’t help solve the problem of academic integrity. Instead, banning AI may contribute to existing achievement gaps. Key reasons not to ban AI from classrooms include:
Banning AI can widen the digital divide between students who have access to these tools outside class and those who don't
AI is becoming increasingly integrated into many professional fields and careers
Low-tech alternatives like handwritten exams can create accessibility barriers for disabled students, English language learners, and neurodiverse students
AI detection tools are unreliable and can produce false positives, particularly harming non-native English speakers
AI detection tools are fundamentally flawed and should not be used to catch or punish students. Key issues include:
Unreliable Accuracy: AI detectors only guess probabilities and regularly produce both false positives and false negatives
Discriminatory Impact: These tools disproportionately flag writing from:
Non-native English speakers
Students with communication disabilities
Writers from marginalized linguistic backgrounds
Anyone taught to write in more formal/standardized styles
Lack of Transparency: The detection algorithms are proprietary "black boxes" that cannot be independently verified
Explicit Warnings from Providers: Major AI detection companies specifically state their tools:
Should not be used as primary decision-making tools
Are not meant for punishing students
Should only be one small part of holistic assessment
Rapid Obsolescence: As AI language models evolve, detection tools quickly become outdated
Easy to Circumvent: Students can easily bypass detection by:
Slightly modifying AI-generated text
Using multiple AI tools
Employing AI tools specifically designed to avoid detection
Creates Adversarial Environment: Using detection software:
Erodes trust between students and faculty
Focuses on punishment rather than learning
May encourage more sophisticated cheating methods
Currently, no software is able to detect AI-generated content with 100% certainty. As you review student submissions, in addition to using feedback from AI-detecting software, check for the following red flags.
AI-generated content is often:
Affected by factual errors
Outdated information
Incorrect information
Citing made-up sources
Features that come with paid upgrades may mitigate these concerns and make plagiarism less obvious.
Not consistent with assignment guidelines. A submission that is AI-generated may not be able to follow the instructions, especially if the assignment asks students to reference specific data and sources.
Atypically correct in grammar, usage, and editing.
Predictable. It follows predictable formations: strong topic sentences at the top of paragraphs; summary sentences at the end of paragraphs; even treatment of topics that reads a bit like patter: On the one hand, many people believe X is terrible; on the other hand, many people believe X is wonderful.”
Directionless and detached. It will shy away from expressing a strong opinion, taking a position on an issue, self-reflecting, or envisioning a future.
For example: when asked to express an opinion, AI tends to describe a variety of possible perspectives, without showing preference for any one of them.
The Hub is offering a professional development workshop to help faculty navigate teaching and assessment in the age of AI. This asynchronous online miniworkshop takes 1-3 hours to complete and provides practical strategies for maintaining academic integrity while acknowledging AI's growing role in education. You'll learn how to create clear AI policies, design both AI-friendly and AI-resistant assignments, and develop effective approaches for helping students use AI tools ethically.
Workshop topics include:
Visit the Academic Professional Development Website to register for upcoming workshop dates.