Colleges are adjusting to a reality that arrived faster than expected. In computer science, students still learn to code, but the emphasis is shifting. Machines now handle much of the syntax and structure. What matters more is deciding what problem is worth solving and how a solution should take shape.
A similar shift is also coming to the humanities. English majors once proved themselves by producing essays and close readings. Now AI can generate both. But it cannot weigh a metaphor, feel irony, or know what grief does to a sentence. It can make words. It cannot live them.
Here are three imagined scenes—examples, not accounts—that suggest how the student, not the software, gives the work its meaning:
Reading for Meaning
A seminar room, February morning, fluorescent lights humming. Students read a story about a lonely robot wandering a city. An AI-generated essay dutifully lists the robot’s parts, its aimless steps, its lack of a home.
A student takes that foundation and begins to build. She notices the word reflection appears three times. She imagines the smudge the robot’s hand might leave on the glass. The silence of the streets echoes louder than dialogue. Her essay turns the robot into a stand-in for people—immigrants, outcasts, anyone unseen.
Here, the machine supplies a scaffold. The student gives it shape and depth. Together, they make something neither could make alone.
Writing with Purpose
Later in the semester, the class is told to draft a speech against racial hatred. One student opens an AI tool. Within a minute, three pages appear: polished, solemn, and bloodless. She doesn’t discard it. She revises.
Lines are scratched out, arrows drawn, words squeezed into margins. Into the smooth surface she inserts memory: a slur on a playground, her grandmother’s silence watching men march with torches on the evening news. The speech is shorter and rougher, but it has a heartbeat.
The machine gives her a neutral rhythm; she transforms it into conviction. The text lives only because she directs it where the machine cannot go.
Connecting Across Fields
The syllabus turns to Orwell’s 1984. Students are asked what the novel means today. An AI summary supplies the bones: Winston hides a diary, O’Brien betrays him, the telescreens never turn off.
The students pick it up from there. One group links Big Brother to the apps that follow their location, sleep patterns, and purchases. Another compares Winston’s rewriting of history to politicians erasing old statements online. One student, glancing at the tiny green light on her laptop’s camera, says, “It’s not the plot. It’s the feeling of being watched.”
The machine recounts, but the students extend. The AI clears the ground; they plant the seeds.
Summary
AI can produce a framework of words, but only people can charge those words with meaning, memory, and connection. The future of English studies isn’t rejecting machines or imitating them. It’s guiding them—using their fluency as material, not replacement.