As a professor, I am often asked: Should students use AI coding agents for their research? I have also been trying to understand this question myself, but until now, I had mostly learned about students’ use of AI indirectly, through conversations with them or by seeing the final results. It is rare to have a long, uninterrupted period in which I can closely observe how they actually work, what they delegate to AI, and how their decisions evolve from day to day. The past three weeks at the Telluride Neuromorphic Workshop offered exactly that opportunity. Students propose their own innovative science and engineering projects, form teams, and work toward a demoable final presentation over three weeks. Here are a few things I noticed: 1. Productivity increased dramatically with AI. This was evident across both engineering and scientific projects. The ability to prototype an idea quickly has never felt so powerful or direct until I sat down with the students and observed their coding pipelines. For instance, one integrated-circuit group developed an asynchronous microcontroller with custom instructions, connected it to event-based sensors, deployed it on an FPGA, and produced a runnable demo with C code and a polished, configurable web interface. Completing all of this in three weeks would have been almost unthinkable a few years ago. Contrary to what people sometimes presume, many amazing ideas still emerge from discussion, while the Claude tokens are burning in the background. 2. Students are under peer pressure to use AI to keep up with the rapid pace of development. I noticed that students were more likely to use coding agents when other members of their teams were already using AI to finish their parts. Once some students begin moving much faster, the pressure to produce comparable results becomes very real. 3. The time budget shifts from debugging to planning. I noticed that students spent significantly more time planning their next steps while leaving much of the tedious plumbing work to coding agents. I saw far fewer of the typical frustrations caused by a “stupid bug,” which allowed students to focus more directly on system design and the actual problem they were trying to solve. 4. AI can be more focused on engineering solutions than scientific exploration. Students sometimes found that coding agents concentrated narrowly on solving the problem stated in the prompt, while overlooking instructions to branch out or explore less conventional possibilities. Instead of substantially modifying an established solution or pursuing a risky new direction, coding agents often preferred a safer implementation that was more likely to work. This is useful for engineering, but it can also discourage the kind of uncertain exploration that leads to genuinely novel research. 5. The biggest concern is understanding. Students increasingly delegate entire tasks to AI, which sometimes leads them to present systems or results that they do not fully understand. This is clearly harmful because it removes both the intellectual ownership and the educational value of academic research. A working demo is not enough if the student cannot explain why it works, where it may fail, and which technical decisions shaped the final result. My conclusion is that AI in research is no longer optional. We need to embrace this change, but we must also separate two objectives that are often conflated: education and research project execution. Students benefit most from AI when they already understand the fundamentals, and when they use it to remove repetitive engineering work so that they can focus more deeply on the core scientific questions. Students should still own the questions, the decisions, and the interpretation, even if AI writes much of the plumbing. My lab may soon become more AI-enabled. The challenge will be making sure the agents accelerate the research without becoming the researchers #AI# #Research# #Engineering# #Education#