The Context Engine: Why the Humanities are the Ultimate Source Code for the AI Era
Here in America, we have spent the last twenty years turning our education system into a content delivery machine. We optimized for information transfer, standardized testing, and the efficient replication of facts. We treated the human brain like a hard drive, measuring its value by how much data it could store and retrieve.
Then we built a machine that can store, retrieve, and synthesize all human data instantly.
If the purpose of education is content delivery, we have just invented a technology that makes the current model obsolete. But if the purpose of education is something deeper, then we are standing on the precipice of a massive correction.
In my recent writings, I’ve argued that we are not witnessing the death of the humanities, but rather their urgent necessity. It’s a shift where the “soft skills” of the liberal arts are becoming the durable “hard skills” of the future economy [1]. But to understand specifically how this applies to the classroom of tomorrow, we need to introduce a new distinction. We need to distinguish between Content Intelligence and Contextual Intelligence.
The Content vs. Context Gap
Generative AI is the ultimate Content Engine. It can produce a sonnet, a lesson plan, or a Python script in seconds. It excels at what I’ve previously described as combinatorial creativity-remixing known elements into new patterns [2].
But AI is structurally incapable of Contextual Intelligence.
It can write a condolence letter, but it cannot know the specific grief of the person receiving it.
It can summarize the causes of the French Revolution, but it cannot feel the hunger that drove the riots.
It can generate a marketing strategy, but it cannot sense the unwritten political tension in the boardroom where that strategy must be pitched.
Context is the domain of the Humanities. History is the study of temporal context. Philosophy is the study of ethical context. Literature is the study of emotional context. Sociology is the study of cultural context.
In a world where Content is infinite and free, Context becomes the premium asset.
Navigating the “Mirror” of Meaning
The danger we face in education isn’t just cheating; it’s the illusion of understanding. As I’ve explored in my work on the “Mirror of AI,” these tools reflect our own inputs back to us, often creating a feedback loop where we mistake the machine’s fluency for genuine intent [3]. Students interacting with AI are entering a world of perfect syntax but zero semantics. The machine speaks fluently, but it means nothing.
The Liberal Arts provide the necessary antibodies against this illusion. A student grounded in rhetoric knows how to deconstruct an argument to find the intent behind it. A student steeped in literature knows the difference between a cliché (which AI loves) and a genuine insight (which requires struggle).
We must teach students to be the Authenticators of Reality. As I discussed regarding authenticity and meaning-making, the value of a work shifts entirely when we know there is no consciousness behind it [2]. The Humanities teach us to discern that difference—to value the human struggle inherent in creation, rather than just the polished output.
Rhetoric: The Source Code of the Future
There is a popular narrative that “Prompt Engineering” is the technical skill of the future. I believe this is a misnomer. Prompt engineering is just Rhetoric in a hoodie.
To get a useful result from a Large Language Model, you must:
Define a persona (Ethos).
Understand the audience and tone (Pathos).
Structure the logic and constraints (Logos).
As I wrote in “STEM Built the Car, Liberal Arts Is Driving It,” this process is indistinguishable from a classical liberal arts exercise [1]. The student who has studied Aristotle or Cicero is infinitely better equipped to “code” in natural language than the student who has only learned rote technical syntax.
We need to stop apologizing for the Humanities and start framing them as the Operating System for the AI age. We are moving from an era of Execution (doing the task) to an era of Orchestration (directing the intelligence). The conductor of the orchestra doesn’t need to play every instrument, but they need a deep, almost spiritual understanding of how the music should feel.
The Education We Need Now
So, what does this mean for Monday morning in the classroom? How do we operationalize “Contextual Intelligence”?
It requires a fundamental inversion of our current pedagogy. For decades, we have treated the essay or the test answer as the finish line. In the age of AI, the answer is merely the starting line.
1. Grading the Prompt, Not Just the Output
If we accept that “Prompt Engineering” is just digital rhetoric, then we must start assessing it as such. In a world of infinite answers, the value shifts entirely to the quality of the question.
The Assignment: Don’t just ask for an essay. Ask the student to submit the prompts they used to generate an essay, along with a critique of the AI’s first draft.
The Assessment: Did they ask a generic question? Or did they frame the prompt with specific constraints, context, and persona?
Grading the prompt allows us to measure the student’s Contextual Intelligence. It reveals if they understood the assignment deeply enough to explain it to a machine. As I touched on in “The Age of Mediation,” we are entering a time where we must become masters of the interface, not just consumers of the output [3].
2. The Return of the Oral Defense
As written text becomes cheaper and easier to manufacture, the ability to articulate thoughts in real-time becomes the gold standard of competence. We should look back to the classical model of the Viva Voce (oral defense).
A student can prompt an AI to write a dissertation, but they cannot prompt an AI to sit in a chair and defend that thesis against a skeptical human professor. The classroom must become a place of debate and dialogue, prioritizing the Socratic method over the scantron.
3. Re-introducing “Intentional Friction”
AI is designed to remove friction. It smoothes out the rough edges of thinking. But as I’ve argued regarding STEM and the Liberal Arts, learning requires friction [1].
We must design assignments that act as “speed bumps” for the mind—tasks that require “Slow Thinking” in a “Fast AI” world. This protects against Cognitive Atrophy, ensuring that while students use AI as a bicycle for the mind, they don’t forget how to walk.
The future belongs to the curious, the nuanced, and the deeply human. It belongs to those who can bridge the gap between the speed of silicon and the depth of the soul. It belongs to the students of the Liberal Arts.
References to my previous work:
I like how this teaches that humanity’s enduring strength lies not in how efficiently we process information but in how we make meaning from it. It reminds us that empathy, curiosity, dialogue, and moral imagination are not technological relics.
They are our deepest cognitive technologies.