About this Research Topic
‘Cognition’ is usually investigated as a performance of the human mind. In contrast, ‘Distributed Cognition’ is a broader concept that assumes that cognition exists both inside and outside the individual mind and has to be analyzed as a holistic system. Typical applications of the concept are for instance computer-supported collaborative learning (CSCL), collaborative tagging on the World Wide Web, air traffic control. A broader definition includes the system of a whole global network of human and technological agents. Some definitions even include any biological and physical entities in order to describe a general holistic intelligent system - the ‚universal brain’. This Research Topic focuses on the role of distribution cognition for learning and behavior change based on human and artificial intelligence in a broad sense, in any case, evidence is requested with a strong theoretical and empirical impact.
The concept of "Distributed Cognition" (DC) is of outstanding importance in face of (a) complex, intelligent processes inside and outside the human mind, (b) the need to see human cognition as only one of several components of intelligent systems and (c) the challenges for the holistic human and digital system to learn and change behavior in order to cope simultaneously with interconnected developments in e.g. digitalization, green deal, social transformation, faked news, (d) artificial intelligence (AI), deep learning, nano-computing, blockchains. big data. Research on DC for at least 30 years has specified many problems to be solved and produced selectively, singular solutions - but still, the heterogeneous conceptualization and modularization has to overcome. By means of current concepts and methods in cognitive, mathematical, computational, experimental, data and open sciences, DC will stimulate future developments in theoretical and empirical basis and applied research in cognitive and computer sciences, esp. wrt learning and behavior change.
In this Research Topic, we invite theoretical, empirical, and/or methodological papers that address topics related to 'Distributed Cognition‘ (DC) in learning and behavioral change based on human, digital and artificial cognition and intelligence (AI). Focusing on learning and behavioral change, some of the topics include, but are not limited to:
- Integrating valid former models into AI for DC
- DC in new domains
- AI-supported design thinking
- Autonomous robots/agents in unpredictable environments
- Interacting mirrored twin representations in DC
- Transforming the learning ecosystem using big data and deep learning
- Learner-centered AI-self-improving tutorial system
- Relationships between DC and distributed action
- AI for open access to digital content in DC
- AI in VR/AR-supported blended distributed learning
- Learning analytics based on AI and big data
- AI-based assessment in "the wild of DC"
- AI in detecting distributed cognitive biases and fallacies
- DC-view on AI in edu-tec
- DC theory as a tool for designing AI systems.
The concept of "Distributed Cognition" (DC) is of outstanding importance in face of (a) complex, intelligent processes inside and outside the human mind, (b) the need to see human cognition as only one of several components of intelligent systems and (c) the challenges for the holistic human and digital system to learn and change behavior in order to cope simultaneously with interconnected developments in e.g. digitalization, green deal, social transformation, faked news, (d) artificial intelligence (AI), deep learning, nano-computing, blockchains. big data. Research on DC for at least 30 years has specified many problems to be solved and produced selectively, singular solutions - but still, the heterogeneous conceptualization and modularization has to overcome. By means of current concepts and methods in cognitive, mathematical, computational, experimental, data and open sciences, DC will stimulate future developments in theoretical and empirical basis and applied research in cognitive and computer sciences, esp. wrt learning and behavior change.
In this Research Topic, we invite theoretical, empirical, and/or methodological papers that address topics related to 'Distributed Cognition‘ (DC) in learning and behavioral change based on human, digital and artificial cognition and intelligence (AI). Focusing on learning and behavioral change, some of the topics include, but are not limited to:
- Integrating valid former models into AI for DC
- DC in new domains
- AI-supported design thinking
- Autonomous robots/agents in unpredictable environments
- Interacting mirrored twin representations in DC
- Transforming the learning ecosystem using big data and deep learning
- Learner-centered AI-self-improving tutorial system
- Relationships between DC and distributed action
- AI for open access to digital content in DC
- AI in VR/AR-supported blended distributed learning
- Learning analytics based on AI and big data
- AI-based assessment in "the wild of DC"
- AI in detecting distributed cognitive biases and fallacies
- DC-view on AI in edu-tec
- DC theory as a tool for designing AI systems.
Keywords: Distributed Cognition, Learning, Behavioral Change, Artificial Intelligence, Human Intelligence, Deep Learning, Mathematical Modelling
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
