The Oral Exam Experiment Worked

Last fall I posted that I was dropping homework from the grade book and adding an oral portion to every exam in the spring. Students were running Engineering homework problems through Gemini and handing in solutions they could not explain when I asked. I was grading a chatbot, so I stopped.

Spring semester is over. It worked. The oral portion runs about ten minutes per student. They pick one problem from their written work and walk me through it. Why mesh and not nodal. What the time constant tells you about the circuit. Where the negative sign came from. I learn more about what a student actually understands in that ten minutes than I used to learn from a semester of graded homework.

A new Lumina Foundation-Gallup study says 57% of US college students use AI in their coursework at least weekly, and one in five use it every day. At the same time, 53% say their school discourages or prohibits it. Daily use is highest among men and among business, tech, and engineering students. The students avoiding AI mostly cite ethical concerns and school policy, so the ones following the rules are falling behind on a tool they will use the rest of their careers.

My position on AI in education is simple. If we are preparing students for the jobs they are about to take, AI has to be in every class. Every engineering job they walk into will expect them to use these tools well. A program that prohibits AI is training students for a job market that no longer exists. The work is not to keep AI out of the classroom. The work is to teach students how to use it, where it fails, and when to check it against first principles.

That still leaves the assessment problem. If students use AI on everything, how do you know what they understand? You change how you measure them. Oral exams catch what written work cannot. In-class paper problems catch it. Hands-on labs, where a student wires a circuit on a breadboard, takes scope measurements, and explains what they are seeing, catch it cold. Take-home essays graded on polish do not catch anything anymore.

The AI can solve the circuit. It cannot explain why this student chose the loop they chose, and it cannot wire the breadboard when the lab is due at five. That is what we should be assessing, and that is the work employers are hiring for.

Opinion: Anthropic, the Pentagon, and a Problem Congress Needs to Fix

This is about as political as I get on gordostuff.com. I write mostly about technology, engineering, education, and the occasional fish story. But this dispute sits at the intersection of AI, national security, and corporate governance, and those topics are worth paying attention to regardless of where you stand politically.

Here is what happened. In January 2026, U.S. special operations forces raided Caracas, captured Venezuelan President Nicolás Maduro, and flew him to New York to face narcoterrorism charges. During that operation, the military used Claude, an AI model built by Anthropic, a San Francisco company that holds a $200 million contract with the Pentagon. Claude was accessed through Palantir Technologies, a data firm whose tools are standard across the Defense Department. After the raid, an Anthropic employee asked Palantir how Claude had been used. That question triggered a confrontation that is now public: the Pentagon wants unrestricted access to Claude for any lawful military purpose; Anthropic refuses to remove safeguards that block use for mass domestic surveillance and fully autonomous weapons. The Pentagon has threatened to cancel the contract and label Anthropic a supply chain risk. CEO Dario Amodei has said the company will not comply.

The Pentagon’s position is straightforward. The “any lawful use” standard it requires is exactly that: lawful. Congress sets those limits. Courts enforce them. OpenAI, Google, and xAI have all reached deals allowing military users access to their models with fewer restrictions. The Pentagon argues that a private company writing its own usage restrictions into a government contract is not a governance model that works in an operational environment, and the Venezuela sequence supports that view. After a successful operation with no American casualties, an Anthropic employee felt it necessary to check whether their product had been used appropriately. That is not a posture compatible with military operations.

Anthropic’s position is not without merit. Amodei argues that current AI is not reliable enough for fully autonomous weapons, and a King’s College London study showing that leading AI models deployed nuclear weapons in 95% of simulated geopolitical crises suggests the concern is grounded. The company’s resistance to enabling mass domestic surveillance of American citizens also has clear constitutional backing. These are not frivolous objections.

The problem is that Anthropic is trying to solve a legislative problem with a contract clause. Mass surveillance of American citizens by the military is a constitutional question. The Foreign Intelligence Surveillance Act, the Posse Comitatus Act, the Fourth Amendment, these are the frameworks that exist for exactly this purpose. If they need updating for the AI era, that is Congress’s job.

Here is the urgency. AI is being deployed in classified military operations right now, today, and the legal frameworks governing its use have not kept pace. The Venezuela operation was not the last time this will happen. The next one may not go as cleanly, and when it doesn’t, the question of what AI was authorized to do, and by whom, will matter enormously. The Senate Armed Services and Intelligence committees should be holding hearings, calling in the AI companies, the Pentagon, and independent legal experts, and drafting legislation that sets clear boundaries. Not someday. Now. A terms-of-service clause in a private contract is not a substitute for law, and the fact that we are currently relying on one is the real problem here.