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#Fragments of thought: AI and learning

by Finn Patraic

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Today, I simply share fragments of reflection on the disruption of Learning through Genai. It is a fragmentary, incomplete, shared work as part of #Workingoutloud.

At the widest level:

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  • Tactical advantages: Efficiency gains in creating learning assets (images, surveys, plans, “expert” content, etc.) which are available for all organizations, but which may not yet be reflected in pricing models, which will be drawn by this capacity.
  • Disturbing advantages: People questioning established practices, experimenting with new approaches and develop greater preparation for change. It is not a question of changing “A” to “B”, but rather the skills and the state of mind of change.

General Shifts:

  • Merchant Skill: What was once rare and expensive is now widely available
  • Knowledge transition: Information has gone from precious to free, with disturbances which are now occurring at higher levels of reflection
  • Fundamental convenience: AI does not only create new content with old methods, but changing the way truth, learning and consumption occur. Different effect mechanisms, not only different results, which will all question the concepts of validity and truth.

Opportunities:

  • Exhaust of inherited systems: Release time and work, although prices (models and structures) collapse behind these efficiency faster than ever. Radical efficiency is probably only table issues now
  • Individualization of learning: Finally, achieving the personalization sought for a long time, on a large scale
  • Contextual learning: Make learning relevant for specific and in progress situations
  • Dialogical learning: Natural interfaces to unlimited knowledge, although this questions models of conformity as well as value and truth systems
  • Asymmetrical disturbance: The real opportunity lies in the change in whole systems, not only optimization in them. This is probably a characteristic of asymmetrical disturbance, no history “could vs could”.
  • Erosion limit: The collapse of the boundaries between learning and performance means that we may need to move an entire function in the support of rehearsal

Domains of transformation or challenge

  • Rapid experimentation: Ability to abandon inherited approaches, the ability to experiment (with governance and codified “creation of meaning” is a rare characteristic.
  • Roles transformation: Going from content creation to the creation of meaning, the construction of the community and to social contexts – it is a difficult – paradigmatic change. Not just an adjustment of roles.
  • Quality on quantity: Focus on high -value connective areas rather than mass producer assets. Question certain beliefs about “value by volume” and “visible vs cognitive”. Metacognitive capacity lies in this space.
  • Rehearsal space: Creation of sandbox environments to prototyper the vocabulary and the skills that underlie performance. Huge opportunity – Huge challenge to property and control systems. Questions models of uniformity and consistency.
  • Data deconstruction: Question the learning data that has been processed as a doctrine. We may well note that a large part of what we have provided more than one obstacle than an advantage because learning is almost entirely reconstructed in the performance space (as opposed to the abstract).
  • Redistribution of tasks: The tasks, roles and unpleasant teams to rebalance the workloads, which will question the structures of power and control, as well as our understanding of the `security ” and how much we really need.

The greatest opportunities come from AI allowing L&D to stop doing unnecessary work and questioning everything that remains.

About JulianStodd

Author, artist, researcher and founder of Sea Salt Learning. My work explores the context of social age and the intersection of formal and social systems.

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