The harm in conflating aging with accessibility

Including older adults as full stakeholders in digital society. </p

From monitoring to engagement: Co-designing future technologies with older adults

Monitoring technology solutions for older people's independent living tends to treat them as passive recipients of technology to be observed by others. This thesis investigated older people's perspectives, exploring how they might reimagine these technologies in their life and future, by involving them in co-design and qualitative research. Studies included older adults inventing their own Internet of Things with kits, writing fictional works about life with future technology, and trialling a collaborative "messaging kettle". This thesis proposes a design approach that shifts the emphasis from perceived needs to bring to light the lived values, agency and aspirations of older people

The stories people tell about the home through IoT toolkits

Stories on the home materialize in many different ways. Simple design scenarios of more efficient smart homes exist alongside more articulated design fictions narrating complex domestic futures. IoT toolkits can be used in co-design to narrate design stories together with people. However, there is little attention on the stories captured in the co-creation process. This paper presents a framework describing, comparing, and assessing design stories. We illustrate the framework through the comparison of the design stories captured from three divergent IoT toolkits in co-design workshops. Three dimensions characterize the design stories emerging from our inquiry: complexity (resolution and scope), likeliness (conceivability and feasibility), and implications (acceptability and consequentiality). This framework contributes towards understanding which properties of IoT toolkits support the emergence of what kind of design story. Our findings help designers to frame expectations when using IoT toolkits and to conceive IoT toolkits that support under-explored qualities of design stories.</p

Older people inventing their personal internet of things with the IoT un-kit experience

We introduce the IoT Un-Kit Experience, a co-design approach that engages people in exploring, designing and generating personally meaningful IoT applications and that also serves as a means to explore IoT kit design through in-home workshops. Un-Kit represents a seemingly uncompleted set of sensors, actuators and media elements that have a decontextualized appearance - unfinished state, undefined purpose and unboxed form. The approach emphasises users contemplating and experiencing the IoT elements in their familiar space through detailed and layered conversation with researchers; rather than focusing on connecting up the kit itself, thus their ideas are not constrained by the kit or their competence with it. We illustrate the approach through in-home workshops with older adults, envisioned users of IoT who have had limited voice in its conception. The Un-kit approach supported participants to lead the process and to imagine new artfully integrated designs, with personally legible interactions and aesthetic qualities that fit their desire. We offer insights for a more situated and responsive approach to design of the IoT and its constituent kits

Designing Interaction with AI for Human Learning: Towards Human-Machine Teaming in Radiology Training

We explore the design of systems that enable humans and machines to operate as teams, exercising their different and complementary abilities to work and learn together. Machine Learning (ML) is now widely used in diverse applications such as medical image reading and autonomous vehicles, but typically, ML systems are not designed with human learning in mind, sometimes eroding or supplanting human skills, creating a whole that is less than the sum of its parts. We propose a new approach to ML/AI system design to foster human-machine mutual learning: synergistic interactions in which machines help people think critically and gain wisdom, while people help improve machine models by reframing ML tasks and immersing them in human-machine-human systems which provide feedback to the AI model while helping humans to learn. By explicitly aiming to increase human skill and wisdom, teaming goes beyond "human-in-the-loop"approaches where humans serve primarily to enhance machine performance. We contribute a conceptual model for human-machine teaming design and use a case study in radiology training to identify five critical considerations for interaction design and for how to make AI interactive: (1) human-machine dialogue (2) labelling and attention (3) problem framing (4) biases, values and affect (5) ethics, agency and human choice.</p