The widespread accessibility of AI and Large Language Model (LLM) assistive technologies—including tools for writing, coding, visual art, and problem-solving—creates both opportunities and significant challenges for teaching and learning. These dynamics make it essential for instructors to deliberately consider whether and how to incorporate AI in their courses, to communicate clearly and repeatedly with students and Teaching Assistants about these decisions. In addition to the guidance on this page, please also refer to the Generative Artificial Intelligence in Teaching and Learning webpage, the Academic Integrity webpage and OTL’s sample AI syllabi policies.
As you navigate AI in your class, we recommend that you focus on designing academic integrity into your course and support student learning by prioritizing trust, class community, transparency, real-world application, student agency, and measuring the learning process.
- Design for Human-Centered Interactions. Create meaningful opportunities for student interaction and discussion that develop critical thinking skills, build class community, and give you a clearer view of students’ learning process over time.
- Focus on the Learning Process, Not the Product. Make assessments more process-oriented with explicit discussion about productive struggle and metacognitive reflections in real-world projects. Incorporate students’ learning goals, agency, interests, peer interactions and project scaffolding into the assessment.
- Discover AI's Uses and Limitations: Dedicate time to exploring and discussing how AI is used in your field, along with the risks related to inaccuracies, biases, and ethics. Discuss the important human-only skills involved in students' future careers in your field.
Create your AI Policy
A joint Academic Senate-Administrative workgroup on AI has recommended that that faculty choose from three-tiers for AI usage in courses: Green permits and/or encourages AI use, Yellow permits AI under instructor-set conditions, and Red means AI is not permitted. The framework provides a shared vocabulary while preserving full instructor autonomy to apply the framework at the course or assignment levels (see OTL sample AI policies). Students depend on instructors to communicate their course AI policies clearly. However, when students are brought into an open discussion about the policies and feel that their input on them is heard, it creates a social contract and builds class community. This removes some common barriers to learning that can lead to cheating or underperformance.
Key Questions to Guide Your Policy
Before drafting your policy, ask yourself:
- What core skills or learning outcomes must students demonstrate entirely on their own?
- Where in the learning process might AI act as a supportive tutor or sounding board?
- How will I know if a student has genuinely engaged with the material?
- How can I discuss these policies with students and get their input on them?
- How can I design academic integrity into my course, e.g. building trust with students, structuring assessments to support learning, securing assessments from AI use.
Navigating Edge Cases
Instructors are sometimes surprised by how UCSB students are using AI and discover that there are many “edge cases” of AI use. Consider what you would do if the following scenarios occurred in your course. What is permissible? Talk with students to create shared expectations and trust.
- Translation & Copyediting: A non-native English speaking student drafts a report in their native language, then uses AI to translate and polish the English prose.
- Reading Assistance: A student uses AI to summarize a complex journal article and explain field-specific jargon in the methods section.
- Brainstorming: A student group prompts AI to generate preliminary outlines and thesis ideas before selecting and writing their presentation topic.
- Exam Prep: A student inputs practice quiz questions into AI to generate ten additional practice items with answer keys.
- Subscription Equity: An assignment permits AI media creation. One student uses a paid, high-end GenAI tool while another uses a basic free tier, resulting in marked differences in output quality.
- Custom AI Tutors: A student creates a personalized study bot by uploading course slides, readings, and lecture notes into it.
- Code Debugging: Students use AI to help write or debug computer programming scripts for homework.
Communicate your AI Policy
To reinforce your course policy, we recommend that you Foster an Environment of Trust and Transparency. A trusting and transparent environment based on human interactions makes students more likely to engage with you and their peers rather than immediately turning to an AI tool. Facilitate discussions with students about their perspectives on appropriate AI use and the importance of human relationships for purposeful interactions in learning. Focus on developing students’ confidence in their own skills and voices, with “courage, honesty, trust, fairness, responsibility, and respect” (Bertram Gallant et al., 2026).
- Use clear, supportive, positive language. Frame your policies around the learning process, wise uses of students’ agency, accountability, and confident skill development over time, not punishment. Share why (or why not) you’ve used AI in your own work. If you use AI, show how you disclose AI usage.
- Explain your "why." Clearly communicate how your approach to AI supports student learning, skill development and confidence, and aligns with the goals of your course.
- Reframe the Conversation: Discuss how students’ “mental weightlifting” or “productive struggle” will build [insert specific] skills and create opportunities for practice and feedback on their learning. If you do allow students to use AI, position it as a tool for specific tasks (e.g. translation support or as a study tool).
Constructive AI Integrations
If you decide to permit AI in your course, consider leveraging it for specific pedagogical goals:
- Study Aids & Practice: Create a shared study “Notebook LM” or custom AI Gemini Gem assistant and upload relevant course materials to generate study questions and practice exercises.
- Critique & Comparison: Teach students how to critique and compare AI-generated code, essays, or analyses with human-created work to analyze nuances in voice, style, and accuracy.
- Genre Analysis: Use AI to generate texts in specialized rhetorical genres so students can analyze genre conventions and audience context.
- Critical Evaluation: Challenge students to detect hallucinations, biases, or logical flaws in AI-generated responses.
Once you’ve decided what is and what is not allowed, consider adapting one of OTL’s sample Generative AI policies for your course and assignments. Please meet with us for feedback on your policy or want to go through more nuanced scenarios related to your course.
Ethical use of AI for Grading and Feedback
Consider the consequences of using AI technologies for grading, feedback and plagiarism detection to make informed ethical decisions about its use in your teaching duties. AI should not be used to provide direct feedback on student work.
AI to Identify Patterns, Feedback, not for Grading
It is possible to use AI to analyze student work for common patterns, generate categories of success or areas of challenge, or create a set of sample comments; however, you must obtain student consent.
Obtain consent
If you plan to use AI to help you identify patterns in student work by uploading that work to AI, you should obtain student permission, and should only use campus-approved AI platforms. You can adapt this sample student consent form for AI platform usage.
Personalize Feedback
AI can provide starting points for your own, human-generated feedback, or it can be used to help refine your draft feedback (e.g. to be more constructive). Your final feedback should be synthesized, edited, and customized to reflect your own voice and specific student needs.
Approach to Suspected AI Misuse
The use of AI technologies falls within the purview of the Student Conduct Code and the Student Guide to Academic Integrity, which states that “[a]ny submission that fulfills an academic requirement must represent a student’s original work…” including “[u]nauthorized use of artificial-intelligence programs to complete course work.” Therefore, student use of AI-assistive technology is not allowed in coursework, theses, dissertations, research articles, etc. unless specifically allowed by the instructor or supervisor. If you suspect unauthorized use of AI technologies, submit an incident report. Be sure to include any samples of earlier/baseline student work to which the work in question can be compared. It is instructors’ shared responsibility to uphold UC Santa Barbara’s academic integrity standards.
AI Plagiarism Detection Software
UCSB does not support the use of plagiarism detection software for several reasons:
- Anti-plagiarism software is highly fallible (Eslit, 2024; Giray, 2024).
- LLMs are advancing at lightning speed with huge injections of capital. Procuring “anti” LLM software contributes to a virtual arms race, with detection software always one step behind what LLMs can produce.
- Submitting student work to anti-plagiarism software may violate students’ intellectual property rights and FERPA protections. When student work is uploaded into a AI-Writing/plagiarism detector database, the student may lose ownership of their work and the instructor/University is unable to safeguard how it is shared and used in the electronic commons.
- Use of anti-plagiarism software can undermine the fundamental relationship of trust that must exist between learners and teachers (Beethan, et al, 2022; Ross & Macleod, 2018). To move from “detection” of LLM use to “prevention,” instructors should consider how students can use LLMs as a tool to support their work and/or craft assignments and activities that cannot be produced by LLMs. While this approach may represent a shift in perspective or assignments, the Center for Innovative Teaching, Research and Learning (CITRAL) offers extensive support for instructors who would like to pursue this approach.
- Instructors and TAs should not use AI-assistive technology for grading and feedback unless the technology is supported by UCSB (e.g. use of GradeScope is permitted, as UCSB has a contract for its use and the technology has been vetted for FERPA compliance).
If misuse of AI is suspected, instructors should report it to the Office of Student Conduct. Cases will be determined based on a "preponderance of evidence," which may include a comparison to students’ previous work, an oral discussion of the submitted work, or other indicators of a substantial change in writing style or content that suggests unauthorized assistance. To this end, it may be useful to scaffold larger assignments with in-class tasks that support student learning and provide samples of their work without AI assistance.
UCSB Campus AI/LLM groups
AI Community of Practice
UC Center for Responsible Machine Learning
UC Mind and Machine Intelligence
References
Beetham, H., Collier, A., Czerniewicz, L., Lamb, B., Lin, Y., Ross, J., Scott, A-M. & Wilson, A. (2022) Surveillance Practices, Risks and Responses in the Post Pandemic University. Digital Culture & Education, 14(1), 16-37
Bertram Gallant, T., Davis, M., & Khan, Z. R. (2026). Academic Integrity in the Age of AI. Cambridge University Press.
Giray, L. (2024) The Problem with False Positives: AI Detection Unfairly Accuses Scholars of AI Plagiarism. The Serials Librarian, 85:5-6, 181-189.
Ross, J., & Macleod, H. (2018). Surveillance, (dis)trust and teaching with plagiarism detection technology. Proceedings of the International Conference on Networked Learning, 11, 235–242.