Generative AI is now part of how students write, research, code, and study — whether or not we plan for it. This guide is a practical, research-grounded starting point for any educator who wants to use AI thoughtfully, protect the integrity of learning, and prepare students for a world in which AI fluency is a baseline professional skill.
This is a living document, written for the 2026 landscape and updated periodically. Tools and policies change quickly — treat specifics as a snapshot, not the final word.
The goal here is not to police AI or to wave it through, but to teach with it deliberately. The instructors who navigate this best tend to do three things: they learn the technology well enough to set informed expectations, they redesign assessment around what they actually want students to be able to do, and they talk openly with students about when and how AI use is appropriate.
A handful of durable principles hold up even as the tools churn:
You don't need to be an engineer, but spend real time with current AI tools before you set rules about them. Your credibility — and your policies — depend on understanding what these systems can and can't do.
"No AI" and "AI encouraged" are both valid — what's not valid is silence. State the rule for each assignment in your syllabus and in the assignment itself, because the right answer differs from a reflective essay to a coding project.
Students will use AI in their careers. Showing them how to prompt well, verify outputs, and use AI critically is part of preparing them — and it reframes the conversation from cheating to competence.
If you use AI to draft a rubric, generate examples, or build a study guide, say so. Disclosing your own use normalizes honesty and signals that students can talk with you about theirs.
Shift points away from easily automated tasks (recall quizzes, formulaic essays) toward work that shows a student's own thinking, process, and voice. This protects learning better than any detector.
Generative AI still fabricates facts, citations, and quotes. Teach students that they own everything they submit — and that "the AI said so" is never a defense for an error.
Don't require students to enter personal or sensitive data into AI tools, and don't assume everyone has paid access. Default to tools with free tiers and no account requirement when you can.
It helps to know what we're actually trying to build in students. Recent scholarship gives us a useful map. Polomoshnov, Masso, and Lobanova (2025) review digital, data, and AI literacy and find that all three share three interrelated dimensions:
Knowing how to operate the tools — and having a working mental model of how AI systems generate their outputs.
Evaluating AI outputs for accuracy, bias, and limitations, and questioning when a tool should be used at all.
The often-overlooked dimension: how we interact with, reason alongside, and make sense of AI as a thinking partner.
Their key insight for teaching: the critical and communicative-cognitive dimensions — not just button-pushing — are what turn AI use into genuine literacy. Assignments that only build technical skill miss most of the point.
For a more concrete checklist, Annapureddy, Fornaroli, and Gatica-Perez (2025) propose twelve generative-AI competencies that progress from foundational to advanced — spanning understanding how generative models work, prompt engineering, evaluating and verifying outputs, recognizing bias and limitations, and reasoning through the ethical and legal implications of use. You can treat these as learning outcomes: pick the few that fit your course and design toward them.
Pick two or three literacy outcomes you genuinely care about in your discipline — say, "verify AI-generated claims against primary sources" and "disclose and reflect on AI use" — and build them explicitly into an assignment and its rubric. Literacy grows when it's assessed, not just mentioned.
A ready-to-use module you can add to any course to build these literacies directly with your students. Faculty can review and test the materials directly from the website, or import the module through Canvas Commons and customize it locally for your courses.
Topics included. The module introduces students to:
The goal is not simply to teach students how to use AI tools, but to help them become informed, critical, and responsible users of AI technologies.
The most reliable response to AI is not detection — it's assessment design. Work that is personal, process-rich, and tied to the real world is both harder to outsource and more worth doing. Some approaches that travel well across disciplines:
Take one existing assignment and add a required reflection: "If you used AI, show your prompts, explain what you kept or rejected, and describe what you learned." This single change converts AI use from a hidden risk into visible, gradable thinking.
Detection tools are unreliable and can falsely accuse students — so lead with clear expectations and humane conversations rather than enforcement theater. When you suspect inappropriate use:
Clear syllabus language prevents most problems. Adapt one of these to your course:
AI not permitted:
AI permitted with disclosure:
Modeling transparent use (yours):
Using AI responsibly means naming its real risks for students:
The most capable tools often sit behind paid tiers. Requiring them can disadvantage students who can't pay. Favor free, no-account options for required work — and remember AI can also level the field by acting as a tutor, translator, or accessibility aid.
AI models learn from human data and can reproduce biased or offensive content. Outputs are not neutral or authoritative; teach students to read them critically.
Assume anything entered into a consumer AI tool may be retained or used for training. Never require students to submit personal, identifying, or sensitive information to these systems.
Copyright questions around AI training data and outputs remain unsettled and continue to move through the courts. AI is generally not recognized as an author; current guidance is to disclose and cite AI as a collaborator following your discipline's standard (APA, MLA, etc.).
The tool landscape changes monthly. Rather than maintain a list that ages, here are durable categories to explore — and a deeper, maintained directory.
My curated, regularly updated directory of AI tools and resources for educators and students, organized by use case. Start here for current, vetted recommendations.
For students: AI courses, certificates, and the AI in Business degree — plus an AI assistant that answers questions about the programs.
Questions, or want to compare notes on teaching with AI? Email me at ggraves@fullcoll.edu or stop by during office hours. This guide grows through conversation — I welcome suggestions and additions.