AI Literacy Faculty Playbook

Teaching AI Judgment, One Habit at a Time.

BE Safe

Know what you're playing with.

I Can: I can explain what AI is and how it works well enough to make smart decisions about how I use it and what I share.

Building Professional Judgment · Student AI Literacy

Purpose

AI tools are increasingly present in coursework and professional training — but confidence with a tool is not the same as understanding it. BE Safe is about closing that gap.

Students who are safe AI users know enough about how AI works to make deliberate decisions: about what they share, about whether a tool fits the task, and about why a confident-sounding response isn't necessarily a correct one. This behavior establishes the foundational awareness that makes every other BE behavior possible.

Students develop this capability through repeated opportunities to engage, reflect, and make increasingly informed decisions over time.

This is a coaching resource — not a grading rubric unless intentionally connected to a course learning outcome.

Developmental Progression

Introduction

First-Year Experience courses

Students are introduced to the idea that AI tools have underlying mechanics worth understanding. They may not yet have examined what AI actually is or how it generates responses. The goal here is awareness — establishing that AI is a system with limitations, not an oracle.

Exploration

First-year and general education courses

Students begin developing a working mental model of how AI generates output. They recognize that AI can be wrong and are starting to think about what that means before acting on its output. They are also becoming more conscious about what they share with AI tools, even if that judgment is not yet consistent.

Application

Program, clinical, shop, and workplace settings

Students understand how AI works well enough to make deliberate decisions every time — not just when prompted. They know why AI gets things wrong, why what they share matters, and why confident-sounding output is not the same as accurate output. That understanding is operational: it shapes behavior in new and unfamiliar contexts, including anticipating where a tool is likely to fail in their field — a nursing student expecting a general chatbot to miss current dosage guidance, or a welding student expecting it to mix up code requirements.

Progression is developmental, not linear. Students may move fluidly between stages depending on the task, the tool, or the course context. The settings under each stage are typical examples, not placements.

I Can Statements

I can describe in my own words how AI generates a response — well enough to explain why it might be wrong.
I can make thoughtful decisions about what personal, academic, or professional information I share with an AI tool.
I can decide whether a tool is reliable enough, and appropriate, for a task before I depend on it.

What This Looks Like · Habits in Practice

Finds out what a tool does with what's typed into it before sharing anything sensitive
Can articulate at least one reason why AI might produce inaccurate or misleading information
Makes visible choices about what to share with AI tools — and can explain those choices
Removes names and identifying details — their own or anyone else's — before using a tool
Considers whether a tool is suited to the task before relying on it — a general chatbot for a medication question, for example

Where This Fits Naturally

Any assignment where students use AI to generate, summarize, research, or troubleshoot
Discussions or activities involving AI-generated content as a starting point
Technical, clinical, and workplace-based courses where confidential information is part of the work
Assignments involving personal, peer, or client information — case studies, reflections, peer review
Orientation and first-year experience contexts where AI norms are being established
Reflection activities that ask students to describe how they used a tool and why

Blackboard Ultra Quick Wins

These are examples — not requirements. Choose the ideas that fit naturally into your course, assignment, and students' level of experience.

Discussion

Ask students where they draw the line on what they share with AI tools — and whether that line changes in their field.

Sample Prompt

"What would you never type into an AI tool? Where is your line, and why? Would that line change in the field you are preparing for?"

Journal

Ask students to document a moment when they used or considered using an AI tool and reflect on what they knew — or didn't know — about how it worked.

Sample Prompt

"Describe a recent time you used an AI tool. What did you assume it could do? What do you know now that you didn't know then?"

Bb Video Studio

Have students record a short explainer describing how AI generates a response — as if explaining it to someone who has never used it before.

Sample Prompt

"Record a 60–90 second video explaining how an AI tool generates a response. Don't look it up — use what you already understand."

Assignment

Have students investigate a tool they already use — what it does with what they type — and write a personal sharing rule based on what they find.

Sample Prompt

"Pick an AI tool you use. Find out whether it saves your conversations, whether they can be used to train it, and who else might see them. Then write one sharing rule for yourself based on what you learned."

AI Chat Conversation

Set up a structured AI conversation where students ask the AI to explain its own limitations — then reflect on whether that explanation itself is trustworthy.

Sample Prompt

"Ask the AI: 'What kinds of questions are you most likely to get wrong, and why?' Then write two sentences about whether you believe its answer."

Knowledge Check

Use low-stakes scenario questions to assess whether students can identify situations where AI use would require extra caution — not right or wrong, but reasoned choices.

Sample Prompt

"Which of these would you paste into a public AI tool, and why? Your own draft essay. A classmate's paper you are peer reviewing. A patient case with the names removed. A screenshot of a group chat."

Supporting Student Growth

BE Safe is not a technology lesson — it is a judgment lesson. The goal is not for students to understand AI architectures; it is for them to know enough to make smart choices.
Avoid framing this as "AI is dangerous." The goal is calibration, not fear. Overconfidence and blanket distrust both get in the way of good judgment.
The most revealing question you can ask is "what happens to what you typed?" Most students have never asked it. Build it into your feedback habits.
In health sciences and other licensed fields, connect BE Safe to confidentiality obligations students already know — patient, client, and student records are protected in AI tools too.
You do not need to be an AI expert to teach this. Modeling your own uncertainty — "I'm not sure how the tool handles this; let's find out together" — is itself a powerful demonstration of the behavior.
Resist the impulse to turn this into AI policing. The goal is to build students' internal judgment, not to catch them misusing tools. Coaching language opens that conversation; punitive language closes it.

Student Reflection Prompts

What do you actually know about how AI generates a response? Where does that knowledge come from — and how confident are you in it?
Have you ever shared something with an AI tool and later wondered where it went? What would you do differently now?
What kinds of information would you be comfortable sharing with an AI tool? What would you hold back? Why?
Whose information besides your own shows up in your AI use — classmates, family, patients, clients? What do you owe them?
How has your sense of what AI can and cannot do changed since you started using it?