AI as Literacy
Rethinking how we prepare campus communities
for an AI-native world.
Doug Langille · Manager, Digital Innovation
Nova Scotia Community College
linkedin.com/in/douglangille · digital.douglangille.ca
doug.langille@nscc.ca · share.douglangille.ca/content/ai-as-literacy
You've done the training.
Has anything changed?
Lunch-and-learns. Prompt libraries. Completion certificates. Governance task forces.
Participation rates go up. Behavioral change does not follow.
"Six months later, people are doing what they were doing before — except now they have a checkbox marked complete."
Digital fluency is not a competitive advantage. Those not fluent will be left behind — and right now our programs are not building fluency. This is not a training quality problem. It is a category error.
The reversion pattern has evidence.
- Dutch longitudinal study (222 students, 8 months) — significant AI adoption decline over time. No stable cognitive framework = reversion to prior patterns.
- Epistemic anxiety — faculty can't audit AI outputs in their domain. Threatening to people who spent 20 years building domain authority. Prompting tips don't touch it.
- The key finding: professional identity shapes adoption more than technical competency.
Tool adoption programs are not designed to address identity. That is the category error.
The word isn't a rebrand.
It's a different category.
Gee (1980s): you are literate when you can think in a mode — not just operate its tools. Each time a new cognitive mode emerged, institutions ran tool adoption programs, watched them fail, then recognised fluency was the actual competency.
- Long & Magerko (ACM CHI, 2020) — 17 competencies; load-bearing dimension: judgment and disposition, not mechanics
- UNESCO AI Competency Framework (2024) — five dimensions; the one that distinguishes literacy from training: human-centered mindset
- The measurement gap — instruments test technical understanding. None reliably measures operative disposition — whether someone engages with AI as a thinking partner
Programs optimize for what they can measure. The load-bearing dimension goes unmeasured — and therefore unaddressed.
Collecting spells is the beginning,
not the end.
Institutions stop at Level 1 not because trainers are lazy — it's the only thing that fits the formats institutions know how to run. That's the structural trap.
This deck was built with AI assistance. If it lands — proof of concept. If it's Level 1 — that's the point.
The person who runs keeps running.
The person losing weight often doesn't.
Behavior is most durable when tied to identity, not outcome. Every identity-confirming experience is a vote for the self-concept. Once the identity shifts, the behavior follows as maintenance — not heroic effort. (Clear, synthesizing the behavior change literature)
— AI Academy study (arXiv, 2025) · 25 faculty · 5 sessions · gains in 6 of 9 dimensions
The goal is not to train people to use AI. It is to help them become people who think with AI.
Honest note: Clear's framework is from habit research. The AI Academy data is the empirical bridge to AI adoption. Strong analogy — still an analogy.
AI is more horse than plough.
A plough is passive. You pick it up, apply force, put it down. A horse has energy you don't — but also its own motion, its own judgment, its own opinion about today's work. You can't just invoke it. You have to show up for it.
Horse + harness + driver + implement = a system that does things neither alone achieves. Remove any one piece and you have a mess, or nothing.
- The harness — the interface between your judgment and the model's power. Most AI failures aren't about the model. They're about the harness being wrong.
- The driver — has to know the direction. Not "what can this tool do" but "where am I going." That's the skill most training ignores.
- Institutions are governing AI like a plough — write the policy, define appropriate use, issue the guidance. You don't policy your horse. You train the driver.
The corral is not the point. The work is the point. The corral exists to make the work safe — not to replace the driver's judgment.
IT has the catbird seat.
Nobody's using it.
IT sees what no other campus function sees: actual tool adoption patterns, the moment of failure, and the populations invisible to professional development programming. That vantage point is the structural advantage — and it's going unused.
- Actual usage data — not survey self-report. IT sees what tools people genuinely use, where they get stuck, what they've quietly abandoned.
- The just-in-time moment — present when someone can't figure something out. That's the teachability moment. No other campus function consistently has it.
- The invisible infrastructure people — admin staff keeping the machinery together. Not in professional development cohorts. In IT's world every day. Enormous unmeasured literacy gap.
- Every sector is a tech sector — every role on campus has an AI literacy need. IT is the only function that sees all of them.
Not territorial. IT brings workflow knowledge and data; CTLs bring pedagogical theory and faculty relationships. The combination is what's missing.
Lead, don't support.
Policy is the corral. You also need to train the drivers. A corral without drivers is infrastructure for nothing.
The access problem is arriving
at the same time.
Valley library closures — July 2026: Hantsport, Kentville, Lawrencetown, Port Williams, Middleton. Static provincial funding since 2020. $1.2B deficit budget.
Libraries are community digital access infrastructure: public computers, digital help desks, informal literacy support for rural, lower-income, and older adult populations least likely to have other access points.
Literacy programs scoped only to enrolled students and employed faculty miss the populations most at risk.
AI literacy is not why libraries are struggling. But closing community digital access infrastructure while AI literacy becomes a workforce baseline is a compounding gap — not a separate story.
For this room: Kentville is in your backyard. The infrastructure loss and the literacy requirement are arriving simultaneously. That is a policy gap.
One question.
- Does the program address all three levels — incantations, alchemy, and wisdom?
- Does it create identity-confirming experiences, not just skill demonstrations?
- Can practitioners reconstruct their reasoning without the AI? Ask this one next meeting. If the room goes quiet — that's the gap.
- Does measurement track derived value, not raw use?
- Is it embedded in ongoing practice, or time-bounded?
The real measure is not adoption. It is: who is this person, and how do they think?
