Maker Movements, Do-It-Yourself Cultures and Participatory Design: Implications for HCI Research

Falling costs and the wider availability of computational components, platforms and ecosystems have enabled the expansion of maker movements and DIY cultures. This can be considered as a form of democratization of technology systems design, in alignment with the aims of Participatory Design approaches. However, this landscape is constantly evolving, and long-term implications for the HCI community are far from clear. The organizers of this one-day workshop invite participants to present their case studies, experiences and perspectives on the topic with the goal of increasing understanding within this area of research. The outcomes of the workshop will include the articulation of future research directions with the purpose of informing a research agenda, as well as the establishment of new collaborations and networks

Materials Libraries: designing the experiential knowledge transfer through prototyping

Experiential knowledge plays a crucial role in exploiting new materials within real contexts, i.e., designing products and applications. As a result, understanding and transferring this kind of knowledge has gained increasing attention, as well as developing new experiential tools addressing this challenge. This contribution investigates the role of physical prototypes in designing new experiential tools for the knowledge transfer of emerging materials and technologies, i.e., Materials Libraries. The analysis is performed through a reflective practice approach based on two practical case studies dealing with new materials from waste for 3D printing. The former Materials Library focuses on the recycling of composite materials from products at their End-of-Life in industrial contexts, i.e., wind turbine blades. The latter one, RepMat Library, is an ongoing experimentation that aims to develop an open source Materials Library to collect new 3D printable materials and applications from waste-based polymers and biomass involving distributed networks and local communities, i.e., makerspaces and fablabs. After briefly explaining the two case studies, this work defines an outline proposal of the main contributions of prototypes in designing new Materials Libraries, which means: (i) generating and detecting the experiential knowledge to transfer; (ii) categorizing and defining the taxonomy of the tool; (iii) testing the experiential knowledge transfer; and (iv) speculating on new possible ways of using Materials Libraries. In short, prototypes were mainly used as a physical learning medium to preliminary tinker with materials and technology, as well as a validating tool for the interaction between the users and the library. Furthermore, prototypes may contribute to envisioning new ways of developing and using Materials Libraries to spread experiential knowledge, i.e., democratizing the design process of the tool by encouraging distributed, accessible, and collaborative work within local communities and distributed networks

Next steps in implementing Kaput's research programme

We explore some key constructs and research themes initiated by Jim Kaput, and attempt to illuminate them further with reference to our own research. These 'design principles' focus on the evolution of digital representations since the early nineties, and we attempt to take forward our collective understanding of the cognitive and cultural affordances they offer. There are two main organising ideas for the paper. The first centres around Kaput's notion of outsourcing of processing power, and explores the implications of this for mathematical learning. We argue that a key component for design is to create visible, transparent views of outsourcing, a transparency without which there may be as many pitfalls as opportunities for mathematical learning. The second organising idea is that of communication, a key notion for Kaput, and the importance of designing for communication in ways that recognise the mutual influence of tools for communication and for mathematical expression

Data-driven Urban Design: Conceptual and Methodological Interpretations of Negroponte’s ‘Architecture Machine’

Nicholas Negroponte and MIT’s Architecture Machine Group speculated in the 1970s about computational processes that were open to participation, incorporating end-user preferences and democratizing urban design. Today’s ‘smart city’ technologies, using the monitoring of people’s movement and activity patterns to offer more effective and responsive services, might seem like contemporary interpretations of Negroponte’s vision, yet many of the collectors of user information are disconnected from urban policy making. This article presents a series of theoretical and procedural experiments conducted through academic research and teaching, developing user-driven generative design processes in the spirit of ‘The Architecture Machine’. It explores how new computational tools for site analysis and monitoring can enable datadriven urban place studies, and how these can be connected to generative strategies for public spaces and environments at various scales. By breaking down these processes into separate components of gathering, analysing, translating and implementing data, and conceptualizing them in relation to urban theory, it is shown how data-driven urban design processes can be conceived as an open-ended toolkit to achieve various types of user-driven outcomes. It is argued that architects and urban designers are uniquely situated to reflect on the benefits and value systems that control data-driven processes, and should deploy these to deliver more resilient, liveable and participatory urban spaces

On Computational Notebooks to Empower Physical Computing Novices

The ever-increasing availability and variety of resources to create physical computing systems keep attracting electronics hobbyists and do-it-yourself enthusiasts. Nevertheless, the prototyping and development of these systems are still challenging to the novices. In this paper, we propose a tool (built on top of the Jupyter computational notebook) as a way for supporting step-by-step assisted learning and knowledge sharing. We extended the Jupyter notebook functionalities and implemented a custom-tailored kernel to seamlessly enable the interaction between the end-user web interface and the Arduino boards. We consider that this approach can effectively support physical computing novices in understanding, writing, and executing the code while empowering them to document and share the development steps they followed

Democratizing Manufacturing: Bridging the Gap Between Invention and Manufacturing

Entrepreneurs and small firms in the U.S. face significant challenges as they scale up their innovations to volume production. Despite innovative new technologies such as 3D printers, the transition to cost-competitive, large-scale manufacturing can be difficult for domestic firms. To assist small U.S. companies to more effectively ramp up production, MForesight assembled more than 30 experts in manufacturing at a workshop on “Democratizing Manufacturing.” The goal of the workshop was to evaluate the gaps and barriers in technology and education that prevent the competitive design and production of engineered components by small businesses in the U.S. This effort is both timely and important because a large fraction of high-value products are now manufactured outside of the U.S. Companies in Europe and Asia are winning bids to manufacture products designed in the U.S. for a host of reasons, including a willingness on the part of their own governments to consistently invest in manufacturing (both infrastructure and human capital). To successfully compete in the global manufacturing marketplace, the U.S. needs to adopt new strategies for education, technology development, and industrial policy.National Science Foundation, Grant No. 1552534https://deepblue.lib.umich.edu/bitstream/2027.42/145152/1/Democratizing-Manufacturing-Dec2016.pd

Democratizing Knowledge Creation Through Human-AI Collaboration in Academic Peer Review

In the rapidly evolving landscape of academic research, artificial intelligence (AI) is poised to revolutionize traditional academic peer review processes and knowledge evaluation systems. We believe that the growing collaboration between humans and AI will disrupt how academics assess scholarly manuscripts and disseminate published works in a way that facilitates the closing of gaps among diverse scholars as well as competing scholarly traditions. Such human-AI collaboration is not a distant reality but is unfolding before us, in part, through the development, application, and actual use of AI, including language learning models (LLMs). This opinion piece focuses on the academic peer review process. It offers preliminary ideas on how human-AI collaboration will likely change the peer review process, highlights the benefits, identifies possible bottlenecks, and underscores the potential for democratizing academic culture worldwide