AI Literacies for
Disciplinary Teaching

Faculty Workbook
Program: Campus Writing and Speaking Program, NC State University
Series: Three clusters — AI Literacy Consultations, Policy Review and Creation, Full Curricular Mapping
Reference: ai.ncsu.edu
How to use this workbook. Each cluster has its own section with activities, reflection prompts, and structured writing spaces. You can work through all three in sequence or enter at the cluster most relevant to where you are right now. Your responses are saved in your browser as you type. Use the Print button at the bottom right to export a PDF for your records.
01

AI Literacy Consultations

Conceptual · Structural · Operative

Cluster 01 · AI Literacy Consultations

Naming the Field

This cluster works through the three-level AI literacy framework in the context of your specific discipline. The goal is not a general orientation to AI tools but a disciplinary analysis — what AI means for how knowledge is made, communicated, and assessed in your field.
1
Conceptual Inventory
Conceptual

Begin with what you can name. A conceptual literacy of AI starts with recognition — what tools exist, what they do, and whose knowledge they encode.

What AI tools are in use in your field — by researchers, by practitioners, by students in your courses? Name as many as you can, and note what each one does.
AI tools you know of in your disciplinary or teaching context
Whose knowledge do these tools encode? Who built them, what training data do they draw from, and whose ways of knowing are centered or marginalized in that data?
Your analysis
2
Structural Analysis
Structural

Structural literacy asks what AI systems reproduce regardless of user behavior. These are conditions built into the infrastructure — not choices individual users make, but effects that follow from the system's design and training.

What does AI reproduce in your field? Consider: epistemic norms (what counts as evidence), language patterns (whose prose style is centered), authority structures (which sources, voices, or methods are weighted), and knowledge hierarchies (what is systematically underrepresented or excluded).
What AI systems reproduce in your disciplinary context
From the research: Roberto Santiago De Roock (2024) argues that AI encodes linguistic white supremacy as a structural condition — not as a user-level bias that awareness corrects, but as a condition of the system's design. What is the parallel structural condition in your discipline?
The structural condition in your field
3
Operative Judgment
Operative

Operative literacy is contextual judgment — not a rule but a capacity. Given your conceptual understanding and structural analysis, what does accountable decision-making look like in your discipline? This includes the conditions under which refusal is warranted.

Contexts where AI use is appropriate in your discipline
Contexts where principled refusal is warranted
What does your discipline's commitment to intellectual labor require of students that AI cannot substitute — not because of a rule, but because the substitution would defeat the learning or epistemic purpose?
The irreducible intellectual labor in your discipline
02

Policy Review and Creation

Reviewing and drafting AI policy with the framework

Cluster 02 · Policy Review and Creation

Policy as Argument

Course and program AI policies are not neutral governance documents. They communicate commitments about intellectual labor, disciplinary identity, and what the course values — often more clearly than the learning objectives do. This cluster asks you to read your existing policy (or the absence of one) as a rhetorical act, and to draft better language grounded in your analysis from Cluster 01.
4
Reading Your Current Policy
Structural
From the corpus: 80+ AI policy documents from 24 institutions reveal consistent patterns. NC State's three-tier DELTA model (prohibit / cite / encourage) encodes contradictory institutional commitments without resolving them. Ohio State reversed a Wave 1 prohibition into a 2025 graduation requirement. Cornell converted governance into downloadable icons. Every document reveals what the institution values — and what it cannot say directly.
Paste or write your current course AI policy statement below (or write "none" if you don't have one).
Your current AI policy statement

Now read it against the three literacy levels. What does this policy ask students to know (conceptual), understand about the system (structural), and do as a matter of judgment (operative)?

Conceptual level: what the policy names or defines
Structural level: what the policy says about AI's conditions
Operative level: what judgment the policy requires of students
What does your current policy reveal about what this course values? What commitment does it make — and is that the commitment you intend?
5
Reviewing Against Program SLOs
Structural

A course AI policy should be legible against the program's student learning outcomes. Misalignment between the two produces policies that enforce norms the program doesn't actually hold — or ignore norms it does.

List the 2–3 program SLOs most relevant to AI use in your course
Where is your current AI policy aligned with those SLOs — and where does it diverge?
6
Drafting New Policy Language
Operative

Policy language that works addresses all three levels. It names the relevant AI tools or categories (conceptual), grounds its limits in what AI cannot do for disciplinary learning (structural), and specifies the judgment it requires rather than reducing to permission or prohibition (operative).

Alignment note: All language you draft here should be compatible with NC State's institutional guidance at ai.ncsu.edu. CWSP can review drafts for alignment before finalization.
Policy Drafting Template
In this course,
AI tools such as
are [permitted / not permitted / permitted with attribution] for
because
Students are expected to
Draft your full policy statement here, using the template as a scaffold
What questions does this draft leave open that you'd want to discuss with CWSP?
03

Full Curricular Mapping

From introductory through capstone

Cluster 03 · Full Curricular Mapping

The Curriculum as Evidence

Curricular mapping asks where AI literacy currently lives in your program's arc — at which level, in which courses, with what consistency — and what the gaps reveal. The map is evidence, not a grade. Its purpose is a shared picture of the curriculum that the program can act on.
7
Curricular Inventory
All Levels

For each stage of the curriculum, note which courses occupy that stage, whether and how AI literacy appears, and at which level (conceptual, structural, operative). Leave cells blank where AI literacy is absent — that absence is part of the map.

Stage Courses at this stage AI literacy present? (describe) Level (C / S / O) Where it lives (syllabus, assignment, discussion)
Introductory
Core requirements
Upper-division electives
Capstone / thesis
Note for program-level mapping: This table works best when completed collaboratively with program faculty. CWSP can facilitate a structured session in which faculty complete this together, using shared SLOs and program documents as anchors. The resulting map becomes a shared program resource.
8
Reading the Map
Structural

A curricular map is useful only if you read it. Answer these questions based on what you've filled in above.

Where does AI literacy instruction concentrate in this curriculum?
Which literacy level is missing or underrepresented?
What does the distribution reveal about what this program currently values — or assumes — about AI and disciplinary learning?
9
Curricular Decisions
Operative

The map produces choices. Based on what you can see, identify two or three specific curricular decisions the program could make to address what's missing or uneven.

Curricular decisions this map suggests
What would each decision require — faculty time, curricular revision, accreditation language, or program governance work?
Across All Three Clusters

Synthesis and Next Steps

The three clusters address the same question at different scales: what does disciplinary, accountable AI literacy look like in your teaching, your course policy, and your program's curriculum? This section asks you to pull the threads together and identify where you want to go next.
10
What Has Shifted
Reflection
What do you understand now that you didn't before working through this?
What question is this work opening that you didn't know you needed to ask?
11
Next Steps Checklist
Operative

Check what you want to do next. These can anchor a follow-up CWSP consultation.

Other next steps you're committing to
Contact CWSP to continue this work

Campus Writing and Speaking Program  ·  NC State University  ·  ai.ncsu.edu

CWSP AI Literacies Platform

Contribute to the Archive

Anonymous · Discipline-indexed · Researchable over time
This workbook is part of the CWSP AI Literacies Platform — a discipline-indexed archive of how faculty across colleges, departments, and programs are analyzing and governing AI in their teaching. Submitting your responses contributes to a growing record that makes disciplinary patterns visible over time, informs CWSP programming, and grounds ongoing research. No name, email address, or identifying institution is collected. Responses are tagged by discipline, institution type, and engagement stage only.
CWSP Program Code (if provided by your liaison)
This code links your submissions across CWSP stages without identifying you or your institution.
CWSP Stage
If you came through AI Literacies independently, select Standalone. If you're unsure, select Stage 1.
Discipline or field
Department or program (optional)
Institution type
Years teaching at college/university level
How did you engage with this workbook?
Which clusters did you complete? (check all that apply)
What is collected and how it is used. Submitting this form sends your workbook responses, discipline, institution type, years of teaching experience, and engagement type to a secure CWSP archive. It does not collect your name, email address, NC State affiliation, or any other identifying information.
  • Responses are used to track disciplinary patterns in AI literacy engagement over time.
  • Aggregate findings inform CWSP programming, workshop design, and ongoing research.
  • Individual responses are not published or shared in identifiable form.
  • This archive may be used as a research dataset. IRB determination is pending. If the project proceeds under IRB review, consent procedures will be updated accordingly.
Submitted. Your responses have been added to the CWSP AI Literacies archive. Thank you for contributing to the platform. You can print a copy of your completed workbook using the button below, or close this window.
Submission did not go through. This is usually a connection issue. Please try again, or contact CWSP directly to submit your responses. Your work is still saved in this browser.