AI

AI is making some old pressures in research louder: more proposals, more submissions, more reviews. That’s a good prompt for a simple question. What problem do we actually want AI to solve, and how will we know it worked?

Many debates about AI turn into “contests” with human: can AI grade, tutor, or advise as well as a person? This issue aims to think about AI in our work from a different premise. AI governance is not about proving whether humans or AI are better at something. It is about how we choose to use technology in our lives.

An FAQ bot I built for a student poster session taught me how an AI that only wants to be helpful can end up suggesting something unethical.

Almost every institution now agrees that decisions and actions about AI in Education belongs inside the disciplines. The harder question is what each discipline needs from it, and what each needs to protect from it.

Collectively, universities stopped waiting for the research to settle before acting on AI. Students have already adopted it, and doing nothing is itself a decision.

Writing is thinking. Coding is thinking. Exploratory data analysis is thinking, and so is creating a good visualization. Working with AI can be thinking too — when you actively engage.

The aiX Faculty Fellowship Program returns for 2026–2027. This year’s fellowship continues to support faculty in designing curricular innovations on teaching with AI and will focus more on curriculum development on teaching about AI within the context of their own discipline. The call for applications is forthcoming — we aim to open applications in early October 2026.

“Adding AI to the curriculum” can mean three very different things.