Whether your students are experienced AI users or new to ChatGPT, they will need context for using AI in your discipline and course. Providing them with basic guidelines of what AI tool is acceptable, when using that AI tool is acceptable, how to use that AI tool ethically can help them avoid miss steps.
AI Literacy should always be the goal: AI Literacy is a philosophical approach aimed at preparing students and our disciplines for developing an agile, ethical approach to this technology. From understanding the basic functions of generative AI to critically evaluating the use of AI and its ethical ramifications, AI Literacy seeks to educate students on the how, why, and when of ethical AI use.
Quick Videos to Share with Students
Here are two quick student-facing videos from the University of Louisville’s Citizen Library.
What is AI Literacy and Why is it Important?
Should I use Generative AI?
Providing Examples and REsources
Like any emerging technology, students may or may not be experienced in using GAI or understand the best approach to doing so. Moreover, information and perspectives tied to this technology are continuously evolving faster than our documentation and scholarship processes. This rapid progression is tied to the very nature of generative AI.
Students need clarity on how to use AI ethically in your courses and in their scholarly work. Consider when using AI in your course might be a helpful tool, and give students examples and context for its use.
The American Association of Colleges & Universities (AACU) partnered with Elon University to develop a free student guide to using artificial intelligence.
Harvard Business Impacts, formerly Harvard Business Publishing, developed a Student Use Cases for AI: Start by sharing these guidelines with your class (2023) resource that can help your students understand four key uses for AI in learning: AI as feedback generator, AI as personal tutor, AI as team coach, AI as learner.
Addressing Academic Integrity
The rapid evolution of generative AI continues to challenge instructional methods for ensuring ethical scholarly student work. There are several approaches to addressing academic integrity in online and in-person courses, from proactive assessment and course design to monitoring student compliance. However, it is important to understand why students make unethical choices and that most instances of academic dishonesty are accidental or are in response to systems of efficiency.
Psychology of Cheating
Cheating Lessons: Learning from Academic Dishonesty
From international expert on the question “why students cheat”, James Lang’s 2013 book has become a practical seminal guide for addressing the foundations of academic integrity. Cheating Lessons: Learning from Academic Dishonesty is available as an ebook in the UNCG Library or in the Jane D. Harris Scholarship of Teaching and Learning (SoTL) Library in the UTLC.
The Opposite of Cheating: Teaching for Integrity in the Age of AI
Expanding on the work of James Lang, the 2025 book from Tricia Gallant and David Rettinger, addresses the unique challenges presented by evolving technology tools. The Opposite of Cheating: Teaching for Integrity in the Age of AI is available as an ebook in the UNCG Library or in the Jane D. Harris Scholarship of Teaching and Learning (SoTL) Library in the UTLC.
Why Students are Using AI in Learning
UNCG students have shared with the UTLC information on how they use generative AI and their concerns for generative AI in their learning spaces.
AI-Resistant Assignments
Can I make an assignment AI-proof, and ensure that students are not using AI when it is not appropriate? There is some debate on whether an assignment can truly be AI-proof, with most scholars asserting that our aim should be creating AI-resistant assignments. AI-resistant assignments provide learners with clear boundaries for AI use, while asking learners to document their learning process.
Tips for AI-Resistant Assignments
- Reflection: assignments that ask students to reflect or connect through reflection to prior learning are more challenging for AI to replicate.
- Process: assignments that are scaffolded requiring learners to demonstrate their process for development (show their work) make AI use visible.
- Collaborative or Team-based: assignments that include peer brainstorming, peer-review, or collaborative development lower AI use while increasing socio-emotional learning.
Quick Resources for Developing AI-Resistant Assignments
EdTech Specialist: 10 Easy Ways to Create AI-Resistant Assignments
Colorado State: AI Resistant assignments
UCLA: Creating Assignments that Eliminate AI Use
GPTZero: How to ChatGPT-proof assignments
UMass: How do I (re)design assignments and assessments in an AI Impacted World
Supporting Academic Integrity in Online Environments
Navigating the impacts of AI can be especially challenging in online learning environments. The growing availability and sophistication of generative artificial intelligence (AI) present unique challenges for promoting academic integrity in online learning environments, particularly in asynchronous courses. Traditional approaches focused primarily on monitoring, detecting, or policing academic misconduct are insufficient to address the underlying challenges AI introduces to teaching, learning, and assessment.
Promoting academic integrity begins with establishing clear expectations, fostering student understanding, and intentionally designing learning experiences that support authentic engagement (see Setting the Expectation for tips). Students may not always recognize the boundaries between appropriate AI-assisted learning and academic misconduct. The distinction between using AI as a thought partner to support learning and using AI to complete work intended to demonstrate a student’s own knowledge, skills, and understanding can be particularly difficult to navigate.
AI Detection Software & Online Proctoring
AI detection software may appear to offer a straightforward solution for identifying AI-generated student work. However, these tools have significant limitations, particularly as generative AI becomes increasingly sophisticated and capable of adapting to individual writing styles. Recent research has raised concerns about the reliability of AI detection tools, including false positives, potential disproportionate impacts on multilingual writers and non-native English speakers, and the ease with which AI-generated content can be modified to evade detection.
AI detection software cannot reliably establish whether a student used generative AI or whether academic misconduct occurred. Detection results should not be treated as definitive evidence of academic dishonesty. For additional information, see MIT Sloan Teaching & Learning Technologies: AI Detectors Don’t Work.
Online Proctoring may be an appropriate option for certain examinations and assessments. However, proctoring technologies and services introduce additional considerations related to accessibility, equity, cost, student privacy, and technical requirements. Faculty should carefully evaluate whether proctoring aligns with the learning objectives and assessment needs of their courses.
- Respondus Lockdown Browser and Monitor are popular options supported by UNCG. Lockdown Browser restricts a student’s browser so they cannot print, copy, or go to another URL or application while taking a test. Respondus Monitor provides additional webcam-based proctoring capabilities when enabled. The challenge with Respondus, it does not work on Chromebooks (popular with UNCG students) and can be flagged by anti-virus software, causing technical delays for students.
- Traditional proctoring arrangements, in which students complete examinations at approved testing locations, may provide an alternative for some online courses. However, these services may introduce additional costs, require travel, and present access challenges for students living in rural areas or balancing employment, caregiving, and other responsibilities. Faculty should consider these potential barriers when determining whether in-person proctoring is an appropriate or equitable assessment option.
The Division of Student Success is exploring additional options for online proctoring. The UTLC will update this resource as new information and institutional guidance become available.
Here are a few other options that may support your students needs:
Designing for Engagement
Thoughtful assignment design is one of the most effective strategies faculty can use to promote academic integrity in AI-influenced learning environments. Rather than relying primarily on detecting unauthorized AI use, faculty can design learning experiences that emphasize meaningful engagement, make the learning process visible, and encourage students to take ownership of their work.
- Reconsider assignments that prioritize task completion over meaningful intellectual engagement. Traditional discussion board formats, such as requiring an initial post and two replies, can become transactional exercises that students may easily complete using generative AI. Kim Manturuk, a faculty member at Georgia State, sncourages instructors to reimagine discussion boards as spaces for meaningful peer interaction and collaborative learning. Consider designing discussions that ask students to review and provide feedback on peers’ work, debate the application of course concepts, collectively analyze case studies, or solve complex problems. Structure discussions to encourage genuine dialogue in which students respond to, question, and build upon one another’s ideas.
- Design assignments that emphasize the learning process rather than solely the final product. Breaking larger projects into smaller, connected stages creates opportunities for students to receive feedback, reflect on their progress, and demonstrate how their thinking develops over time.Manturuk suggests making meaningful participation in an assignment more straightforward than engineering an AI prompt to complete it. Scaffolded assignments can help accomplish this by incorporating checkpoints, drafts, feedback, and revision opportunities throughout the learning process. Consider incorporating process-based feedback that allows students to demonstrate their decision-making, explain their reasoning, and reflect on how feedback informed their work.
- Consider shifting from a small number of high-stakes examinations toward more frequent, lower-stakes opportunities to demonstrate learning. The University of Albany encourages instructors to consider how assessment structures can influence students’ decisions about academic integrity. When a substantial portion of a course grade depends on only a few examinations, the consequences of a single poor performance can create considerable pressure to succeed. Frequent, lower-stakes assessments create opportunities for practice, feedback, and improvement while reducing the consequences of individual setbacks. These assessments can also help faculty identify learning challenges earlier and support students before they fall behind.
- Help students understand not only what they are expected to do, but why the learning experience matters. In online asynchronous courses, where opportunities for spontaneous conversations about the purpose of coursework are limited, transparent assignment design can help establish relevance, build trust, and strengthen students’ investment in their learning. Clearly communicate how assignments connect to course learning objectives, what knowledge or skills students will develop through completing them, and how those capabilities relate to future coursework, professional practice, or real-world challenges. When students understand the value of the learning process, they may be more motivated to engage meaningfully with an assignment rather than prioritize the efficiency of producing a finished product with AI.
- Design assignments that invite students to make their reasoning, decision-making, and learning processes visible. Rather than assessing only the final product, ask students to explain how they approached a problem, evaluated competing perspectives, applied course concepts, or arrived at their conclusions. Consider incorporating brief process reflections, annotated drafts, decision-making rationales, or opportunities for students to explain how they used feedback or AI tools during an assignment. These approaches can help faculty assess students’ understanding while reinforcing the importance of intellectual engagement and ownership of their learning.
- When appropriate to course learning objectives, incorporate assignments that ask students to critically evaluate AI-generated content rather than simply avoid using AI. Students might compare an AI-generated explanation with course readings, identify inaccuracies or unsupported claims, evaluate disciplinary assumptions, or revise an AI-generated response using scholarly evidence. These activities can help students develop AI literacy, critical thinking, and disciplinary judgment while reinforcing that AI-generated information requires careful evaluation. Faculty should clearly distinguish between assignments where AI use is an intentional part of the learning experience and those where students are expected to demonstrate independent knowledge and skills.
Quick Resources
- University of Colorado Boulder — AI Assessment Scale
- University of Alberta — AI and Assessment
- University of Calgary — Generative AI in Assessment
- Kofinas et al. (2025) — The Impact of Generative AI on Academic Integrity of Authentic Assessments
- Peer and AI Review + Reflection (2025)
- How AI Shaped Students’ Perceptions of Assessment within Online Learning Environments (2026)
Setting the Expectation
Using common language across courses in a discipline can reduce learner confusion on when to use AI. Perkins, et. al (2024) The AI Assessment Scale (AIAS): A Framework For Ethical Integration of Generative AI In Educational Assessment offers a research-informed framework and scale for AI integration in courses.
Easy to Use Framing:
This handy Creative Commons licensed graphic and PDF for the AIAS framework is free to use and offers easy to understand language outlining course-level use of AI.
Ideas for Consideration:
- Students may lack a clear process for creating scholarly work. Consider surveying students at the beginning of the semester to share their current process for completing their coursework. For instance, how do they approach research? What’s their experience in creating citations? How do they tackle bigger projects like papers and presentations? Knowing this information can suggest relevant ways to discuss how to approach course work and ethical uses of generative AI.
- Students may be new to generative AI technology. Don’t assume that because generative AI is in the news that your students already know how to use it. If there is concern about students using this technology unethically, discuss the technology as a class. Discuss academic honesty and ethical uses of this technology. Explain what role generative AI should play in the course. Add language to the course-level information and policies that clarifies expectations of generative AI use.
- Students rely on instructors to develop a scholarly process. Scaffold large assignments to help students learn the process of learning and critical thinking. Use smaller, reflective assignments to help students learn and practice specific steps in the scholarly process.
- Students need to know how generative AI will be used in their field of study. Discuss with students how professionals are using this technology. Consider bringing in a guest speaker from your field to discuss this technology and ask questions.
- Students value assignments that are authentic and give students agency. Consider revisiting some assignments that encourage students to analyze the output of AI generators. For example, instead of asking students to write a paper, generate one or more papers from a generative AI source and ask students to analyze the output with some guiding questions.
- Students don’t come to college with the intention to cheat. Create a class environment that encourages students to feel comfortable approaching you and others when they need support.