Academic integrity responsibilities are typically fragmented across institutions; divided between professional teams, central units, and academic Schools. This siloed structure limits collective capacity to detect, respond to, and prevent misconduct effectively. It also limits effective assessment design which responds to integrity threats and reduces targeted study skills for students.

We present an organisational model that directly connects previously separate teams and functions – integrity and misconduct detection, learning and assessment design, study skills support – into an integrated, data-informed integrity ecosystem. Each team’s outputs actively shape another’s priorities: misconduct data flags emerging threats in specific assessment formats; integrity reviews drive targeted study skills sessions; student support insights feed back into central integrity education; and learning design work, including guidance on appropriate AI use, informs the development of new detection approaches.

This integrated model has produced more consistent misconduct reporting, faster identification of emerging threats, and more coherent guidance to academic staff.
This poster presents our model, the data flows that sustain it, and the structural changes that made it possible, as well as our learnings along the way!