The researchers in the DESIRE4EU project propose the use of an eco-optimized PLA/Flax-based PCB manufacturing substrate that could be recycled or degraded after use due to the organic, biodegradable nature of the material. However, having a biodegradable PCB is not enough, as we need to rethink electronics design to better fit specific use cases, avoiding a one-size-fits-all philosophy. This paper includes a set of learned lessons in the form of design guidelines extracted from the experience of a small-scale manufacturing of a set of biodegradable microcontroller boards. These lessons outline contemporary technical limitations of biodegradable PCBs, which the authors trust will be overcome during the development of the mentioned EU project by 2027. This should be a first step towards reducing e-waste in the not-so-far future. This positioning paper states that, in the current ecological crisis, the different engineering communities need to rethink their priorities in order to produce ecology-friendly innovations by keeping concepts such as just enough computing and ecooptimization in mind. In particular, when talking about the TinyML community, we suggest carefully considering the hardware’s limiting factors presented by computational power, or radio communication when designing new Edge devices so that they could use 2-layers biodegradable PCBs. In parallel to the hardware discussion, the authors bring up issues emerging from using bloated inference software production workflows, which have a very direct impact in the ecology due to the computation power needed for embedded machine learning software production. This paper suggests the adoption of ondevice training to minimize the energy consumption and dependance on connected toolchains during programming. 

Embedded machine learning (ML) on low-power devices, also known as "TinyML," enables intelligent applications on accessible hardware and fosters collaboration across disciplines to solve real-world problems. Its interdisciplinary and practical nature makes embedded ML education appealing, but barriers remain that limit its accessibility, especially in developing countries. Challenges include limited open-source software, courseware, models, and datasets that can be used with globally accessible heterogeneous hardware. Our vision is that with concerted effort and partnerships between industry and academia, we can overcome such challenges and enable embedded ML education to empower developers and researchers worldwide to build locally relevant AI solutions on low-cost hardware, increasing diversity and sustainability in the field. Towards this aim, we document efforts made by the TinyML4D community to scale embedded ML education globally through open-source curricula and introductory workshops co-created by international educators. We conclude with calls to action to further develop modular and inclusive resources and transform embedded ML into a truly global gateway to embedded AI skills development.

Electronics prototyping platforms such as Arduino enable a wide variety of creators with and without an engineering background to rapidly and inexpensively create interactive prototypes. By opening up the process of prototyping to more creators, and by making it cheaper and quicker, prototyping platforms and toolkits have undoubtedly shaped the HCI community. With this workshop, we aim to understand how recent trends in technology, from reprogrammable digital and analog arrays to printed electronics, and from metamaterials to neurally-inspired processors, might be leveraged in future prototyping platforms and toolkits. Our goal is to go beyond the well-established paradigm of mainstream microcontroller boards, leveraging the more diverse set of technologies that already exist but to date have remained relatively niche. What is the future of electronics prototyping toolkits? How will these tools fit in the current ecosystem? What are the new opportunities for research and commercialization?  

There are currently few ways to reliably and objectively assess olfaction outside of the research laboratory or clinic. The COVID-19 pandemic has highlighted the need for remote olfactory assessment; in particular, smell training at home is a promising method for olfactory rehabilitation, but further methodological advances might enhance its effectiveness and range of use. Here, we present Exerscent, a portable, low-cost olfactory display designed primarily for uses outside of the laboratory and that can be operated with a personal computer. Exerscent includes Radio Frequency Identification (RFID) tags that are attached to odor stimuli and read with a MFRC522 module RFID reader/antenna that encodes the odor in order to provide adaptive challenges for the user (e.g., an odor identification task). Hardware parts are commercially available or 3D printed. Instructions and code for building the Exerscent are freely available online (https://osf.io/kwftm/). As a proof of concept, we present a case study in which a participant trained daily to identify 54 odors, improving from 81% to 96% accuracy over 16 consecutive days. In addition, results from a laboratory experiment with 11 volunteers indicated a very high level of perceived usability and engagement. Exerscent may be used for olfactory skills development (e.g., perfumery, enology), and rehabilitation purposes (e.g., postviral olfactory loss), but it also allows for other forms of technological interactions such as olfactory-based recreational interactions.

Since the advent of Artificial Intelligence (AI) and Machine Learning (ML), researchers have asked how intelligent computing systems could interact with and relate to their users and their surroundings, leading to debates around issues of biased AI systems, ML black-box, user trust, user’s perception of control over the system, and sys- tem’s transparency, to name a few. All of these issues are related to how humans interact with AI or ML systems, through an interface which uses different interaction modalities. Prior studies address these issues from a variety of perspectives, spanning from under- standing and framing the problems through ethics and Science and Technology Studies (STS) perspectives to finding effective technical solutions to the problems. But what is shared among almost all those efforts is an assumption that if systems can explain the how and why of their predictions, people will have a better perception of control and therefore will trust such systems more, and even can correct their shortcomings. This research field has been called Explainable AI (XAI). In this studio, we take stock on prior efforts in this area; however, we focus on using Tangible and Embodied Interaction (TEI) as an interaction modality for understanding ML. We note that the affordances of physical forms and their behaviors potentially can not only contribute to the explainability of ML sys- tems, but also can contribute to an open environment for criticism. This studio seeks to both critique explainable ML terminology and to map the opportunities that TEI can offer to the HCI for designing more sustainable, graspable and just intelligent systems.

Today’s digital world requires students to gain skills in collaborative problem solving and digital literacy. One approach is to teach people how to design computational artefacts that require both electronics and programming. Physical computing platforms offer an endless amount of possible opportunities for people to design and develop technological artefacts. However, many times students are overwhelmed when trying to learn both software and hardware simultaneously. The students struggle to be innovative and creative in their projects. Also, they focus on mastering the tool and following instructions for existing projects rather than being able to creatively explore the tool and understand the process of designing and developing new artefacts. For that reason, we aimed to answer the question: What type of tools and activities can be developed to support university students creative exploration of physical computing? Programming and electronics are fundamental design disciplines in today's digital world, and therefore they should be taught through design activities rather than limiting education to textbook readings and laboratory exercises. We introduce our process of designing activities combined with a supportive tool to ease these challenges. The activities and tools were developed iteratively in three phases with a series of workshops with 126 students and teachers. The tool consists of a set of paper cards that provide necessary details (hints) about the electronics and software and help provide structure for the students to conceptualise how their artefact interacts. We additionally, introduced a learning Jigsaw pattern (orchestration script) for the later intervention that enabled individual students in the groups to focus on design, hardware, or software. For evaluation, we used the Creativity Support Index (CSI), which is a psychometric survey designed to assess the support of the creative process. The instrument investigates collaboration, efforts worth the result, exploration, immersion, enjoyment, and expressiveness. The results between the phases showed improvement with the use of the refined versions of the cards and orchestration of the learning activity. This study has demonstrated that design activities can provide a more accessible approach for the introduction of physical computing to students from various majors. Moreover, learning physical computing through design activities allows the learner to develop computational and design thinking skills for collaboratively solving problems.

This paper introduces the experiments realized by Arduino Education in the field of educational robotics. The paper, written as a collection of annotated exemplars, covers a series of prototypes, kits, and full educational programmes which were tested with students of different ages and educators. Some projects are of a do-it-yourself (DIY) nature, a property we came to describe as DIY-ness, while some others have been manufactured and served to tens of thousands of students. There are however things in common that can help others in the conceptualization, development, and deployment of educational robotics initiatives.

This article explores the history of contemporary Spanish Do-It-Yourself (DIY) spaces (hacklabs, hackerspaces, fab labs, makerspaces and after-school academies) and the growth of each type since the 1990s. The development of these types of spaces is reflected against the commodification and commoditisation of DIY in Spain. The article argues that the removal of the political layer of the early Spanish DIY techno-tactical movements allowed a higher degree of dissemination within society in general, while reducing the emancipatory poten-tial of these new spaces. However, the analysis of the degree of com-modification and commoditisation of types of spaces in relation to the amount of spaces per type shows an anomaly for makerspaces. The authors reflect upon this anomaly and whether a data set enlarge-ment could correct it. For their analysis, the authors constructed a data set of events of the Spanish DIY history through the design of an ad hoc mixed method. Tracing events and spaces could not be done in a simple way due to the long time span of the study: older spaces existed in the pre-social network days, and new ones exist only in dedicated platforms for niche communities of practice. This method of tracing events and spaces is another contribution of the article as it could be used to make similar causality analyses of historical data in other case studies.

At the first signs of the Covid-19 pandemic, the uncertainty around the global stock of medical supplies sparked a response in the DIY communities around the world. In the case of Spain, a community called Coronavirus Makers (CVM) appeared to supply ventilators and personal protection equipment (PPE) to hospitals and people in need. This paper explores the evolution of this community-driven development, detailing the patterns proposed by members of the group acting as design experts to tackle different problems. More specifically, the paper uses face shields, the most produced PPE in Spain, as a boundary object to highlight the relationships between individuals, institutions, and companies. These objects of design, being devices for medical use, must overcome validation at the technical level. Authors will also explore some of the controversies surrounding the transfer of these products from horizontal innovation networks to traditional production companies. 

This paper introduces different experiences, from experiments to commercial kits, looking at how to make IoT easier to understand by users from a variety of age groups. The hereby presented trials cover highly complex technical platforms. Connectivity, data collection, visualisation, or analysis are concepts that participants in workshops and courses have been introduced to with different degrees of success. The different experiments are finally compared offering other scholars and curriculum creators a point of departure to further work.