Throughout K-8 I had an excellent academic experience. Off the top of my head - Mrs. Hebert, Mrs. Elsden, Mrs. Halla, and Mr. Valliere, my first male teacher in fifth grade, followed by Mr Pasqualini, Mr Crean, Mr Bash, all ran their classrooms like the work mattered. My mother taught 8th grade English with the same conviction. Then came 9th grade.
My high school had lost its accreditation due primarily to over crowding. The city responded with double sessions while a new school was being built. Eleventh and twelfth grades ran from around 7 am to noon, ninth and tenth ran from around 1 pm to 6 pm. The teachers worked hard, and looking back I can see how much effort they put in. But half the students had mentally left the building before they walked in the door. Teaching into that kind of indifference is exhausting in a way that effort alone cannot fix. It sucked. By the time we moved to the newly constructed high school for 11th grade, I had pretty much checked out. I wanted to just get through it and move on to college. The light bulb didn’t go on until I got there, where I felt challenged and found out I could excel, just like I had in grades K-8.
I’ve spent over forty years on the other side of that equation, teaching courses like circuit analysis, photonics, and robotics. So when I read Ben Gomes’s recent interview in Forbes, his argument sure made a lot of sense. Gomes is Google’s Chief Technologist for Learning and Sustainability, and he spent 21 years building Google Search. His point: the biggest problem in education is motivation, and AI cannot solve it. High-achieving people are almost never unlocked by an algorithm. They are unlocked by a person, usually a teacher who made them feel the work mattered. Once that happens, tools can accelerate everything. Without it, nothing moves.
What those double-session years showed me is that teacher motivation and student motivation are not separate problems. The teachers were trying. The system had stripped away a lot of the conditions that make student motivation possible, and no amount of individual effort fully compensates for that. Faculty working hard into a wall of disengagement will not hold together indefinitely. That is not an argument against AI tools. It is an argument for taking retention seriously as the central issue.
A six-month pilot with Northern Ireland’s Education Authority found teachers using Google’s AI tools saved an average of 10 hours per week. Google has committed 50 million in AI education grants and is building training materials for teachers across the United States. Those are real gains. But Gomes frames the stakes correctly: there is a projected worldwide shortage of 44 million teachers. That gap exists because the profession burns people out before they finish a career. If AI recovers enough time to make the job sustainable, it addresses the shortage at the source. If that recovered time just gets absorbed by more administrative load, nothing changes.
Gomes also makes a point about what education should teach as AI handles more of the mechanics. Programming syntax matters less. Conceptual thinking matters more. How do you decompose a problem? How do you think about abstraction? Those questions don’t disappear because a tool can write the code. In engineering education I see this directly. The students who do well aren’t the ones who memorized the most procedures. They’re the ones who understand why the procedures work.
Mr. Valliere, my Mom and the rest didn’t teach me content I can still recite. They taught me that learning was worth doing. Two years in a broken system came close to erasing that, despite the genuine effort of the people in front of the room. College restored it. The difference, every time, was whether the conditions existed for learning to take hold. AI tools that give teachers back their time and energy are worth every dollar. The goal should not be a faster classroom. It should be keeping the people in it.
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