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This work presents Magnetform, a shape-change display toolkit designed to enable exploration of movement in soft materials. The toolkit allows designers with no technical knowledge to leverage their material expertise to experiment with shape-change. We present the toolkit design, and case studies with two design studios who used the toolkit for 15-days each. Through the presentation of their process, we reflect on two main themes: empowering designers to participate in shape-change exploration; and the developing practice of designing objects which integrate motion. We situate this work as part of the growing efforts in the TEI community to involve designers in the evolution of shape-changing interfaces, and demonstrate how material-oriented designers could contribute to this field in which materiality plays a major role in.

Technology plays a major role in reproduction of art and craft. While increasing their accessibility to the public, reproduction also raises questions concerning authenticity and quality. 3D Scanning and Printing have extended reproduction boundaries, positioning it as a candidate for taking part in the artistic process. In this work we suggest to consider the imperfection in the reproduction process as a distinguishing element that adds to authenticity. We present a "chain of digital reproductions", a series of 3D printed objects, beginning from a reproduction of the original Ennion's Blue Jug. Next to the last reproduction, a 3D Printer and scanner will print the next reproduction in the chain. The exhibited work will demonstrate how reproductions can gradually become independent of the original artwork and have their own unique meaning and aesthetics.

One of the challenges faced by healthy older adults is experiencing feelings of not "being-seen". Companion robots, commonly designed with zoomorphic or humanoid appearance show success among clinical older adults, but healthy older adults find them degrading. We present the design and implementation of a novel non-humanoid robot. The robot's primary function is a cognitive word game. Social interaction is conveyed as a secondary function, using non-verbal gestures, inspired by dancers' movement. In a lab study, 39 healthy older adults interacted with the prototype in 3 conditions: Companion-Function; Game-Function; and No-Function. Results show the non-verbal gestures were associated with feelings of "being-seen", and willingness to accept the robot into their home was influenced by its function, with game significantly higher than companion. We conclude that robot designers should further explore the potential of non-humanoid robots as a new class of companion robots, with a primary function that is not companionship.

Machine Learning services are integrated into various aspects of everyday life. Their underlying processes are typically black-boxed to increase ease-of-use. Consequently, children lack the opportunity to explore such processes and develop essential mental models. We present a gesture recognition research platform, designed to support learning from experience by uncovering Machine Learning building blocks: Data Labeling and Evaluation. Children used the platform to perform physical gestures, iterating between sampling and evaluation. Their understanding was tested in a pre/post experimental design, in three conditions: learning activity uncovering Data Labeling only, Evaluation only, or both. Our findings show that both building blocks are imperative to enhance children's understanding of basic Machine Learning concepts. Children were able to apply their new knowledge to everyday life context, including personally meaningful applications. We conclude that children's interaction with uncovered black boxes of Machine Learning contributes to a better understanding of the world around them.

Children are growing up in a Machine Learning infused world and it's imperative to provide them with opportunities to develop an accurate understanding of basic Machine Learning concepts. Physical gesture recognition is a typical application of Machine Learning, and physical gestures are also an integral part of children's lives, including sports and play. We present Scratch Nodes ML, a system enabling children to create personalized gesture recognizers by: (1) Creating their own gesture classes; (2) Collecting gesture data for each class; (3) Evaluating the classifier they created with new gesture data; (4) Integrating their classifiers into the Scratch environment as new Scratch blocks, empowering other children to use these new blocks as gesture classifiers in their own Scratch creations.

The biological prototyping revolution is in motion, and new tools are needed to empower HCI researchers, designers, makers, and bio-enthusiasts to experiment with live organisms. We present OpenLH, a liquid handling system that empowers users to conduct accurate and repetitive experiments with live biology in a sterile, open, and affordable way. OpenLH integrates a commercially available robotic arm with custom 3D printed parts, a modified pipette, and a visual block-based programming interface. The system is as accurate as commercial liquid handlers, capable of repetitive tasks in micro-scale accuracy, easy to operate, and supports multi-materials including biomaterials, microorganisms and cell cultures. We describe the system's technical implementation and two custom interfaces. We demonstrate the system's impact for the HCI community with two use cases that include experimentation with live biology in non-traditional fields: visual design using pigment-expressing E.coli, and beer brewing experiment using serial dilution in home context.

Opening encounters are an integral element of everyday social interaction, and are essential for forming and maintaining social relationships between people. We present an abstract non-humanoid robotic object called the Greeting Machine, designed to communicate positive and negative social cues in the context of opening encounters. The design includes a small ball rolling on a larger dome, with a custom gear and lever mechanism that supports a variety of subtle movements. Gestures were designed with movement experts, and were evaluated using a physical first-person qualitative study. Our findings reveal that an abstract robot designed with no specific metaphor can effectively take part in opening encounters. Furthermore, a minimal brief movement, designed as an Approach or Avoid gesture, may be enough to evoke positive and negative experiences. The ability to create opening encounters with low Degree of Freedom abstract robots is promising, both due to the low complexity, low cost, and design flexibility of such devices, and due to the possible generalization of the Approach and Avoid gestures for a variety of morphologies.

Machine Learning (ML) processes are integrated into devices and services that affect many aspects of daily life. As a result, basic understanding of ML concepts becomes essential for people of all ages, including children. We studied if 10--12 years old children can understand basic ML concepts through direct experience with a digital stick-like device, in a WoZ-based experiment. To assess children's understanding we applied an experimental design including a pretest, a gesture recognition training activity, and a posttest. The tests included validating children's understanding of the gesture training activity, other gesture detection processes, and application to ML processes in daily scenarios. Our findings suggest that children are able to understand basic ML concepts, and can even apply them to a new context. We conclude that ML learning activities should allow children to sample their own examples and evaluate them in an iterative way, and proper feedback should be designed to gradually scaffold understanding.

Outdoor play has been proven to be beneficial for children's development. HCI research on Heads-Up Games suggests that the well-known decline in outdoor play can be addressed by adding technology to such activities. However, outdoor play benefits such as social interaction, creative thinking, and physical activity may be compromised when digital features are added. We present the design & implementation of a novel digitally-enhanced outdoor-play prototype. Our evaluation with 48 children revealed that a non-digital version of the novel outdoor play object afforded social play and game invention. Evaluation of the digitally-enhanced version showed reduced collaborative social interaction and reduced creative thinking when compared with baseline. However, we showed that specific sensing and feedback features better supported outdoor play benefits. For example non-accumulated feedback was shown to increase collaborative play and creative thinking in comparison to accumulated feedback. We provide evidence-based recommendations for designers of outdoor play technologies.

We present the initial design process of Scratch Nodes, a sensor-based prototype designed to augment children's social-physical outdoor play. Scratch Nodes has two main components: a hardware device and a tablet-based coding environment. The prototype was designed for 8-12 year old children with the goal of encouraging physical play, social interaction, and "changing the rules" through coding. We extend prior work in the Heads-up Games (HUG) domain by adding a real-time coding environment that directly controls the hardware device, empowering children to change the game's rules in real-time. We argue that the combination of physical play, social interaction, and coding strikes the right balance between the societal need to increase outdoor play & enhance computational thinking skills on one hand and children's need to play, measure, and define their own rules on the other. We present our initial design and implementation process as well as our insights from a preliminary evaluation with six children who tested the prototype.

Shape-changing materials development strives to generate motion in new ways, extending the traditional motor-based and mechanism-based techniques. We present a system that uses continuous magnetic force to create movement in a range of soft materials such as textile, foam, paper or silicon. The system has two parts: a control platform and a multi-material layer. The control platform is an array of electromagnets controlled by a microcontroller. The multi-material layer is made of a soft material with embedded ferromagnetic elements. The subtle electromagnetic force manipulates the embedded ferromagnetic material, resulting in continuous and organic-like movement in the material layer. We hope our system can empower designers to generate expressive movement with a broad range of materials. In our own work, we aim to extend our Empathy Objects research with soft materials, creating physical objects that convey emotion through expressive movement.