AI upskilling for research practitioners.

The brief

Research teams feel AI moving faster than anyone can track alongside a full workload. Professional societies and university organizers commissioned sessions with a clear ask: teach working practitioners to use AI properly in their actual work, not in the abstract.

What we teach

Four things, drawn from the work itself. Developing research ideas and writing protocols with AI. The applied pipeline that follows: data collection, analysis, and data science workflows, including causal inference. Manuscript preparation: drafting that stays human-sounding and accurate, finding and verifying citations, literature review. And the foundations: using assistants like ChatGPT and Claude across personal and work workflows, from competitive research to everyday automation.

How it’s taught

Sessions run one to two hours, live and interactive all the way through: questions at any point, back-and-forth, hands-on practice. Everything is tool-agnostic and adapted to what the organization already licenses; a team that runs on ChatGPT gets a ChatGPT-shaped session. No lecture-and-leave.

Where it’s landed

Over 300 practitioners across 10+ workshops and webinars so far, with attendees from more than 60 institutions in 17 countries, including Harvard, UC Berkeley, Columbia, University College London, and the U.S. Environmental Protection Agency. The sessions keep getting commissioned, and the curriculum keeps pace with the tools.