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.
Universities are moving from negotiating AI access to designing the conditions around it. This week’s issue looks at a growing question: What should students understand before they begin using institutionally provided AI? Requiring AI literacy before access offers one answer and raises a harder question about how campuses will measure what students learn.
This week’s post opens with two projects from our own aiX lab — on treating LLMs as objects of statistical inquiry, and on designing reliable agentic AI workflows — alongside the usual roundup from across higher education.
This week’s post centers on three threads: the durability of learning — whether learners’ gains through AI use survive once the tool is removed; assessment as the load-bearing response to that problem; and the redefinition of entry-level work as AI skills become a hiring baseline.
This week’s post examines two converging disruptions: accountability when AI mediates consequential decisions, and AI’s growing strain on the knowledge and talent ecosystems — peer review, open-source software — that it was built on.
This week’s post examines the widening gap between near-universal AI adoption and institutional readiness — from Microsoft and Gallup survey data to the platform competition at ISTE 2026.
