Trust, Sustainability and Learning@Scale

It is not overstating matters to say that humanity finds itself at an inflection point. The interlocking crises can feel overwhelming (ecological; political; financial; medical; technological; educational...), with recent leaps in AI closing the gap between human and machine cognition, raising many issues, including of course, educational questions. If a plausible diagnosis for our predicament as a species is "failure to learn", praxis questions assail us. What qualities should we cultivate most urgently, in which contexts? What literacies equip teachers and learners to engage critically with AI? How do we track progress meaningfully, at scale? And very pragmatically, when and why do people deem our tools trustworthy enough to trial, and if robust, embed sustainably into their teaching and learning practices?

Taking a complex organisation as a microcosm of these challenges, I offer some reflections through the prism of nine years running a university learning analytics innovation centre. We invent and evaluate analytics and AI targeting student qualities that transcend the disciplines, such as critical and reflective writing, teamwork, dispositions, and sense of belonging. Without trust we cannot deploy at scale, motivating our use of methods from human-centered design and deliberative democracy to build common ground among diverse stakeholders.

Perhaps the approaches and lessons learnt in this small but nonetheless complex system can scale fractally, offering insights for our wider challenges. Whatever the scale, it seems that trust, sustainability and learning reinforce each other, and must shape how we conceive, design and deploy our learning infrastructures.