Welcome to the 2026–2027 academic year. To those returning, it is good to have you back. To those arriving for the first time, we are glad you are here.
This week we celebrate Lydia Kavraki and Rebecca Richards-Kortum, who are both now members of all three United States national academies: engineering, science, and medicine. About thirty people in the world hold that distinction, and two of them are our colleagues. I hope you will join us in recognizing them later this week. Lydia saw that planning a robot's path through a cluttered space and searching the shape space of a molecule are, at their core, similar problems. Rebecca kept asking a question that technology development too rarely asks: who a device is actually for. Neither was honored for producing results quickly, and both built their careers in a world where answers were harder to come by.
We live in a different world now, one in which a well-phrased prompt can produce an answer in seconds, articulate and often correct. So the question I would put to all of us this year is not whether to use the machine, which is already settled, but how do we grow as scholars in an age when answers are free? I use scholar to include the first-year student as fully as the professor with a lab, and I include myself.
If answers are abundant, the answer is no longer the scarce thing. What stays scarce is discernment: noticing that a confident reply is subtly wrong, that a polished paragraph rests on a false premise, that a question has been framed to bury its assumptions. And before any of it, knowing which problem is worth posing at all. When we rent an apartment, we pay for years and own nothing in the end. Careless use of the machine is renting an intellect: the argument, the worked solution, the finished draft are delivered to us on demand and leave nothing behind. And we end up competing with the very machine we rented, for the work we never built the judgment to do.
This is not a student problem. The temptation is strongest on the busiest among us, and I feel it myself. Nor is it a complaint about the machine, which, used well, makes a demanding thinking partner. What seems free to us is not free: these systems carry real costs in human labor, energy, and water. There is another cost, more directly our own: what we give up when we let the machine do the thinking we came here to learn to do.
We are not meeting this unprepared. Colleagues across the school are rethinking assignments and assessments, work that is difficult, largely invisible, and being done well. It also has a home in our strategic plan. The second pillar of Vision 2030 calls on us to develop the whole engineer and computer scientist along three dimensions: the Strong Foundation, the Experiential Bridge, and the Human Bridge. Each describes something a machine cannot supply and a person has to build. As the home of computing and AI at Rice, we have a responsibility beyond our own majors. We are designing new courses on AI for all students, regardless of major, to expand AI education across the Rice student community.
And still, no plan and no amount of faculty effort supplies the one thing that matters most, which is that we have to want to learn. That applies to a first-year student, to a professor rebuilding a course, and to a dean deciding each morning whether to think a problem through or accept the first fluent answer he is handed.
We begin this year alongside colleagues who reached something very few people ever reach, and they got there by wanting to know. In an age when answers are free, that is still the whole of it.
It is going to be a great year, and I am glad to be spending it with this community.
The Message from the Dean of Engineering and Computing at Rice University is published quarterly during the academic year, and is shared with our students, faculty, staff and friends.