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Human and Artificial Cognition: Systems, Ecologies and Culture

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How is digital technology shifting the way that we learn within organizations? How do we prepare ourselves and our organizations in local and global ecologies of change? How do we act in that tension between current demands and future demands? How do we do significant cognitive work with sophisticated technologies? Dr. George Siemens points out that we are beginning to think with technology – it is more than a tool that we use, it is a tool that uses and shapes us. As we look towards the future of organizational performance, we need to consider technology agents in addition to human employees and participants.  

 

Integrating artificial and human cognition in a coherent manner 

Organizations must consider distributed cognition across AI and human agents. It is essential to integrate the cognitive work of humans and AI. To do this effectively, organizations must ask themselves what machines do better than people, and what people do better than humans.  

It is crucial to know ‘what works where’. In terms of organizational growth, it is important to scale human-centered topics through humans, and scale technology-related ideas through technology.  

What can AI do?  

AI is effective in structured, computation heavy, rules bounded activities. Practically, these include: recognize images, speak well, and identify hidden patterns in scientific data and delineated creative work. 

AI does cheat. It is very good at solving problems but does not always solve it in the way you want it to be solved. For example, AI technologies that are trained to classify skin lesions as potentially cancerous could learn that lesions photographed next to a ruler are more likely to be malignant.  

Ideally, AI functions to amplify human intelligence by having machines do things that it is much better at than anything humans can do on their own.  

What can humans do?  

Humans are best at developmentally consequential tasks and activities. This includes creating and sustaining cultures, creating and sharing art, and building social relationships. If we push this off to technology, we lose our ability to self-reflect and embrace our ‘humanness’.  

The majority of our human knowledge is stored within culture. AI does not have the capability to understand culturally-embedded ways of being and ways of doing.  

Living and learning in a Post-Learning Age  

This is a world where we think and learn with machines, not just alongside them. It is a world where what we know is less important than how we are connected to ongoing knowing. Most importantly, it is a context where sensemaking, meaning making, and wayfinding become the primary knowledge activities. We must learn how to engage in ambiguous and unknown environments, which means that we engage in very different organizational learning practices rather than, say, take a module on this upcoming organizational change. This style of learning is intensely personal.  

Big takeaways: How does this information help you?  

  • Learning within organizations is often preconceived, pre-structured, devoid of context and devoid of personal connection. This is partially due to legacy structures, which are antagonistic to innovation and change.  
  • Organizations must acknowledge increasing levels of automation, and delegate appropriate work to AI and to humans. Essential human-work includes culture and cultural ecologies, being-ness, and social-emotional learning across the lifespan.  
  • Learning in a Post-Learning Age is more about learning orientations, rather than specific knowledge. Supporting learning ‘rivers’, rather than learning ‘lakes’.  

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Harvard Graduate School of Education