Generative AI for Social Learning: Reflective Surfaces

Part of learning is the creation of ‘meaning’, building out our understanding of the world around us, and evolving that understanding through addition, fracture, and sharing. Sometimes we learn something new that invalidates the old, and sometimes it simply layers on top of it. We can view this process in various ways: as one of disturbance, of exploration, of change. But it’s not simply a solo cognitive activity.

Learning can be social, collaborative, co-created, and highly dynamic: not simply what ‘i’ think, but influenced by what ‘we’ think too. Indeed there is a certain elasticity around our learning, in that we can hold incomplete concepts and ideas, or contextual ones where our understanding or usage shifts dependent on what we are doing (consider how our relationship with ‘fairness’ is contextual).

In ‘Engines of Engagement: a curious book about Generative AI’, we wrote a whole chapter on ‘Learning’, and the ways that Generative AI will impact the experience and effectiveness of learning.

Dialogue is part of this: not simply the dialogue we have with ourselves, our internal dialogue (something i explore in the Social Leadership Daily work), but also the dialogue with each other and, in the age of the Story Engines, dialogue with our devices too.

We considered the commoditisation of dialogue, and how Generative AI makes dialogic enquiry both widespread, and a solo activity: how you can prototype ideas, and rapidly iterate thought, in ‘conversation’ with the story engines.

The context of the Social Age sees a shift in the nature of knowledge itself, and our relationship with it. Perhaps a more pragmatic, albeit more fragile one. Democratised in terms of access, but more fractured in terms of validation and ownership. The creation of knowledge within communities – the increased globalisation of the local, tacit, and tribal – the desire of Organisations to access the social knowledge, and risks thereof.

In this context, i have increasingly differentiated between ‘knowledge’ and ‘meaning’, and indeed am inclined at times to describe learning – in Organisational contexts – as primarily the creation of meaning. So less about facts and figures, which are now mobile and come to us, through our distributed knowledge infrastructure, but rather more about the understanding, insight, and opportunity that we discover through ‘sense making’ processes.

We can support this at a procedural and methodological level through the creation of artefacts of thought, at both individual and collective level. I’ve been doing this recently with my co-author, Sae Schatz, on a leadership programme, whereby we are using the Generative AI tools not simply for analysis (sentiment, shared narrative, tone), but also for reflection. Creating images that represent and reflect back the story. And then using these for a further loop of learning.

This notion – that we run through successive loops, and can create artefacts of each one – helps us to explore and discover alternative ways of knowing. Partly because it removes our notion of one ‘perfect’ answer, in favour of a moveable boundary of knowing. The idea that our understanding may be a movable feature within a broader landscape.

I quite like this more dynamic view, which partly represents the nature of Social Collaboration, and how we each act upon the other, but also as it reflects the overall more dynamic view of Knowledge and hence ‘meaning’ in the Social Age. All of which indicates that we cannot thrive by teaching people ‘one way of knowing’, but rather need to build the capability of ‘sense making’ and understanding the mechanisms by which we create individual and shared ‘meaning’.

Our intention is to research and write more about these ‘Reflective Surfaces’, including some worked case studies: currently Sae, Geoff and i are talking about potentially creating a practitioners guide, or other resources to go alongside the book. The integration of Generative AI tools into our everyday practice will impact not simply systems and process (of efficiency and effect), but also our social practices of learning, and we hope to explore this further.

#WorkingOutLoud on Social Learning and Generative AI