If AI Can Complete Coursework, What Is the Purpose of Effort?
Effort matters when it helps students become more capable.
AI can help students finish coursework faster, explain difficult ideas, or supply instant answers. But when a system does the thinking an assignment was designed to cultivate, completing the task no longer means developing the capability. Learning requires what cognitive scientists call desirable difficulties—retrieving ideas from memory, trying solutions, spotting mistakes, and revising an approach.
The real question isn’t whether students do work the hard way, but whether they have the chance to practice the reasoning that builds lasting understanding.
If AI Can Answer Every Question, What Becomes of Curiosity?
When AI can answer questions in seconds, curiosity still matters. Students need opportunities to notice what they don’t understand, follow their questions, and use AI to explore rather than end the inquiry.
If AI Can Teach, What Becomes the Role of Faculty?
A student uses AI to get unstuck on a difficult reading. That can help—but a clear explanation alone doesn’t guarantee learning. As AI takes on more instructional tasks, faculty’s judgment, course design, and support for students’ deeper understanding matter even more.
Prompting Is a Form of Thinking
Prompting is more than a technique for getting better AI responses. It requires students to define the problem, identify what they know, recognize what they still need, and decide what role AI should play. When students remain responsible for those decisions, prompting becomes a form of metacognition—and a way to learn alongside AI without handing over the thinking.
AI Confidence vs. Accuracy
The danger isn’t that AI is wrong. It’s that it sounds right.
AI doesn’t just give answers, it gives confident answers. Clear. Structured. Fluent. Often faster and more polished than what most of us would produce on our own. And that’s exactly the problem.
From a cognitive psychology perspective, this isn’t surprising.