Assuring quality learning in a gen AI-integrated future: The role of adaptive capabilities

This document was commissioned by TEQSA to support institutions as they reflect upon and address the impact of generative artificial intelligence (gen AI) on learning outcomes. The advice contained within this document is not part of TEQSA’s suite of Guidance Notes and is not intended to be prescriptive. There will likely be considerable variation in how individual institutions approach the assurance of quality learning. This, and the related resources (listed below), are therefore offered to support institutions in considering their approach to assuring students are meeting learning outcomes, while avoiding formulaic solutions. Together these resources provide insight, from sector experts, into how and why quality assurance may need to change in response to evolving gen AI technologies.

Jason M Lodge, Paula de Barba, Louise Ainscough, Jonathan R Brazil, Jaclyn Broadbent, Daniel Ebbert, Sarah Frankland, Florence Gabriel, Dragan Gašević, Ted Hennicke, Lisa-Angelique Lim, Sally A Male, Negin Mirriahi, Eduardo A Oliveira, Helena Pacitti, Mladen Raković, Joanne Russell, Daniel Taylor-Griffiths, Suijing Yang

06/2026

Tertiary Education Quality and Standards Agency