NC State · Campus Writing and Speaking Program
A three-level framework for higher education governance, faculty development, and institutional decision-making
When institutions describe their goal as building AI literacy, the phrase often obscures more than it reveals. Literacy in relation to AI is not a single capacity that a learner either has or lacks. It is a layered set of competencies — conceptual, structural, and operative — each requiring the preceding level as its analytical foundation. The three-level framework presented here names those layers precisely, so that faculty development programs, governance bodies, and institutional policy work can target the right level for the right purpose.
The Three-Level AI Literacy Framework
Click any level panel to expand its definition
Conceptual literacy is the capacity to recognize, name, and describe AI systems with precision, identifying system type, tracing the relationship between training data and model output, and distinguishing among architectures, applications, and classification schemes. Knowledge at this level is primarily technical and definitional. Its operative function is to make the vocabulary of AI legible as a site of decision-making: every term in AI discourse encodes a choice about whose cognitive practices count as intelligence, whose outputs count as correct, and whose contributions to the training set are treated as ground truth. Conceptual literacy is present when a practitioner reads a system description and identifies not only what the system does but whose knowledge the system was built to recognize, so that the definitional question "what is this?" becomes inseparable from the power question "whose knowledge is encoded?"
Structural literacy is the capacity to analyze conditions inherent to AI systems as information infrastructure, including the encoding of dominant-language and dominant-culture norms in training data, the reproduction of existing social hierarchies in model outputs, the concentration of AI development within specific corporate and geopolitical contexts, and the mechanisms by which AI assessment instruments scale linguistic hierarchy as if it were cognitive capacity. Analysis at this level moves from description to diagnosis: not what the system is, but what the system reproduces and for whose benefit. Structural literacy is present when a practitioner traces the chain of production and control behind a system, reads ownership, optimization targets, and data provenance as analytically prior to any question of deployment or use, and treats those conditions as objects of institutional inquiry rather than background facts.
Operative literacy is the capacity to exercise contextual, accountable judgment about AI use in specific situations, including the evaluation of outputs, ethical reasoning about deployment, assessment of institutional governance frameworks, and principled refusal where structural conditions warrant it. Judgment at this level is irreducibly particular: the right action is determined by the specific conditions of a specific situation, analyzed through the structural and conceptual capacities the preceding levels provide. Operative literacy is present when a practitioner articulates not only a decision but the structural analysis grounding it, and accepts institutional accountability for that reasoning. Refusal theory (Simpson, 2014; Campt, 2017; Benjamin, 2019) names what operative literacy looks like at institutional scale: not non-compliance but accountable rejection of conditions that reproduce harm, grounded in analysis rather than preference.
Cole, K. K. (2026). AI literacy is not one thing: Conceptual, structural, and operative levels for higher education governance. Pedagogies: An International Journal. https://doi.org/[to assign at publication]
The AI Literacies Extension · NC State CWSP
Generative AI has changed what it means to ask students to write. Faculty across disciplines are rethinking assignment design, academic integrity policies, and what authorship requires in their fields. The WOLF AI Literacies extension provides a structured space to work through those questions — not with prescriptive answers, but with frameworks, discussion prompts, and design tools grounded in communication pedagogy.
The extension does not advocate for a single approach to AI in the classroom. It is designed for faculty who want to think carefully about what they actually value in student writing and speaking — and to build assignments that make those values explicit, whether AI tools are permitted, restricted, or somewhere in between. It is built on the same foundations as WOLF: it foregrounds what communication is for, who it is for, and what disciplinary participation requires. It treats AI not as a threat to be managed but as a condition of writing instruction that demands more explicit and intentional pedagogy — which is what WOLF has always argued for.
The AI Literacies extension is appropriate for faculty who are revising course policies in response to generative AI tools, redesigning assignments to better assess disciplinary communication rather than AI-generatable outputs, trying to articulate to students (and themselves) what writing and speaking are actually for in their courses, or interested in teaching AI literacies as a transferable skill within their discipline.
It is available as a standalone engagement or as an extension of an ongoing WOLF partnership. Faculty who complete the extension alongside a WOLF engagement often find that it sharpens the Inquiry and Design phases: the questions AI raises about authorship and assessment make visible assumptions about communication that were previously implicit.
The faculty workbook at the center of the extension guides work through four areas:
Liaisons facilitate the extension using the same model as WOLF: sustained conversation, iterative design work, and documentation of decisions and rationale. The workbook is designed for liaison-facilitated use, but faculty are welcome to work through it independently. Contact Kirsti Cole at CWSP to begin or to connect your work to a full WOLF engagement. More information at cwspwolf.com/ai-literacies.
Governance Application · AI Detection Software
The three-level framework is not only a faculty development tool. Institutions are currently making procurement and policy decisions about AI-based academic integrity software — decisions with direct consequences for student populations most vulnerable to algorithmic misclassification. The framework below applies the three-level analysis to that specific governance question, making visible what a conceptual-literacy-only governance process misses and what a structurally and operatively informed decision requires.
Consequence: a governance body with conceptual literacy but not structural literacy distributes the costs of the tool's failure modes — a 61.3% false positive rate for non-native English writers — onto the students least protected from the consequences of a false accusation of academic misconduct.
Before any structural analysis is possible, the governance body must be able to accurately describe what it is being asked to purchase.
The structural question is not whether the tool works in general. It is whose writing the tool's design encodes as the standard against which all text is measured.
Operative literacy does not prescribe a single answer. It specifies the conditions a principled answer must meet.
Warranted when structural conditions reproduce linguistic hierarchy, process-based alternatives exist, and structural conditions are not remediable by policy overlays.
This is principled operative refusal in the framework's specific sense: accountable rejection of conditions that reproduce harm.
Requires all of the following to be explicit conditions of the vendor contract:
Adoption without these conditions in place is not conditional adoption. It is procurement without structural accountability.
When the structural analysis is incomplete, defer the decision and return to Level 2.
A procurement timeline imposed by institutional urgency is not grounds to skip structural analysis. Urgency distributes costs; it does not eliminate them.