Damian Chapman

Currently and in synchrony with being Kingston University's Head of Design School, Damian is a PhD candidate in the RCA School of Design pursuing research for systems design.

Using a Human-centered approach, he is breaking down the corpus of the research into co-design phases through a Design Development Framework applied to a Circular Simulation Measurement model. Transforming production processes and improving visualisation of complex data, the impact of the research is to explore how a design approach can enable richer / deeper understanding of complex data streams than existing approaches, in remote condition monitoring in the energy industry

Humankind is entering a new phase in its evolution through the emergence of digital tools used in cyberspace, industry and domestic life. The proliferation of Artificial Intelligence automating the collection, distribution, analysis and communication of data from business, utilities and social-media is machine learning with design processes now capable of functioning without reliance on human intervention. Cyber-physical systems now seamlessly interact via the Internet of Things (IoT), enabling humankind to enter a data driven knowledge period in its history that is reshaping the building blocks of the crucial social bonds in a collective society.

Together, human and machine, through collaboration and language in the form of algorithms, interacting with new technologies and constructing human and machine hybridity are augmenting both life-enhancing efficiencies and industry productivity. However, within Cyberspace, a new Knowledge Space of collective intelligence is emerging with a growing problem of data overload and the need for methods of data visualisation to make sense of the increasing lakes of raw unstructured data requiring context. It is here both in Cyberspace and our physical world that design can enable witness marks, log experience, build trust repair and develop mechanisms for society to function and grow. 

Within the mass of data overload is the crucial individual’s trace in the Knowledge Space, a fundamental field especially from the perspective of Artificial Intelligence as a tool in the Internet of Things in context to the agency and productivity to be gained from a human-centred perspective. It is within the growing collective intelligence forming the Knowledge Space that my doctoral research seeks to intervene. 

The significance for the research is that data overload is a problem for everyone coping with the mass of data harvests and the data available yet there appears to be an inability to use this massive opportunity and knowledge to support progress in increasing useful explorative outcomes or to explore new forms of interaction and information. Artificial Intelligence provides a possibility to gather information and help provide an individual trace, a surviving mark or trail of interaction within the Knowledge Space.

Disruptive approaches for enhancing engagement in online communities are changing dramatically to mobile online-users engaging with new forms of collaboration and interaction in mapping heightened functionality, capacity and productivity through data-driven experiences of the environments in which people inhabit. Consequently, there is a gap in knowledge and need for innovation in identifying smart and efficient forms of interaction design to explore an individual trace in the Knowledge Space approach to re-imagine, frame and prototype new models of design.

Commencing with a comprehensive assessment of the philosophy behind collective intelligence forming the Knowledge Space and IoT and Web 4.0 enhancing interaction through Artificial Intelligence and machine learning, this research will go on to consider what practices might have to gain from human and machine hybridity, thereby offering original insights into how interaction design and usability can be improved through the Individual trace in the Knowledge Space as a framework and visualisation tool.

The research methodology is a Mixed Methods approach utilising an Iterative Design Development Cycle. An ethnographic participatory approach for insights in user-experience will inform a cycle of development. Here, I am breaking down the research into co-design workshops that enable the building of prototypes to develop a communication toolkit. The toolkits are tested to elicit new forms of interactivity, construct imaginative interaction design and facilitate new forms of individual trace in the Knowledge Space. Both qualitative and quantitative feedback will inform further improvement cycles in a series of case studies. Thematic pattern analysis from findings will enable the research to sort, filter and re-structure new knowledge by mapping an organisational data hierarchy. This work will be designed to visualise the contextualised journeys of data which defines who passes data on to who, when and why… by clarifying and implementing the concept of ‘Trace’.

Key terms: Knowledge Space, Individual trace, Cyberspace, AI, Design.