Envisioning Futures of Design Education: An Exploratory Workshop with Design Educator

The demand for innovation in the creative economy has seen the adoption and adaptation of design thinking and design methods into domains outside design, such as business management, education, healthcare, and engineering. Design thinking and methodologies are now considered useful for identifying, framing and solving complex, often wicked social, technological, economic and public policy problems. As the practice of design undergoes change, design education is also expected to adjust to prepare future designers to have dramatically different demands made upon their general abilities and bases of knowledge than have design career paths from years past. Future designers will have to develop skills and be able to construct and utilize knowledge that allows them to make meaningful contributions to collaborative efforts involving experts from disciplines outside design. Exactly how future designers should be prepared to do this has sparked a good deal of conjecture and debate in the professional and academic design communities. This report proposes that the process of creating future scenarios that more broadly explore and expand the role, or roles, for design and designers in the world’s increasingly interwoven and interdependent societies can help uncover core needs and envision framework(s) for design education. This approach informed the creation of a workshop held at the Design Research Society conference in Brighton, UK in June of 2016, where six design educators shared four future scenarios that served as catalysts for conversations about the future of design education. Each scenario presented a specific future design education context. One scenario described the progression of design education as a core component of K-12 curricula; another scenario situated design at the core of a network of globally-linked local Universities; the third scenario highlighted the expanding role of designers over time; and the final scenario described a distance design education context that made learning relevant and “close” to an individual learner’s areas of interest. Forty participants in teams of up to six were asked to collaboratively visualize a possible future vision of design education based on one of these four scenarios and supported by a toolkit consisting of a set of trigger cards (with images and text), along with markers, glue and flipcharts. The collaborative visions that were jointly created as posters using the toolkit and then presented by the teams to all the workshop participants and facilitators are offered here as a case study. Although inspired by different scenarios, their collectively envisioned futures of what design education should facilitate displayed some key similarities. Some of those were: Future design education curricula will focus on developing collaborative approaches within which faculty and students are co-learners; These curricula will bring together ways of learning and knowing that stem from multiple disciplines; and Learning in and about the natural environment will be a key goal (the specifics of how that would be accomplished were not elaborated upon.) In addition, the need for transdisciplinarity was expressed across the collaborative visions created by each of the teams, but the manner that participants chose to express their ideas about this varied. Some envisioned that design would evolve by drawing on other disciplinary knowledge, and others envisioned that design would gradually integrate with other disciplines

Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes

Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians' decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of "Unremarkable Computing", that by augmenting the users' routines technology/AI can have significant importance for the users yet remain unobtrusive. Our field evaluation suggests clinicians are more likely to encounter and embrace such a DST. Drawing on their responses, we discuss the importance and intricacies of finding the right level of unremarkableness in DST design, and share lessons learned in prototyping critical AI systems as a situated experience

Design concepts for the development of cooperative problem-solving systems

There are many problem-solving tasks that are too complex to fully automate given the current state of technology. Nevertheless, significant improvements in overall system performance could result from the introduction of well-designed computer aids. We have been studying the development of cognitive tools for one such problem-solving task, enroute flight path planning for commercial airlines. Our goal was two-fold. First, we were developing specific systems designs to help with this important practical problem. Second, we are using this context to explore general design concepts to guide in the development of cooperative problem-solving systems. These designs concepts are described

Exploring the future of data-driven product design

Connected devices present new opportunities to advance design through data collection in the wild, similar to the way digital services evolve through analytics. However, it is still unclear how live data transmitted by connected devices informs the design of these products, going beyond performance optimisation to support creative practices. Design can be enriched by data captured by connected devices, from usage logs to environmental sensors, and data about the devices and people around them. Through a series of workshops, this paper contributes industry and academia perspectives on the future of data-driven product design. We highlight HCI challenges, issues and implications, including sensemaking and the generation of design insight. We further challenge current notions of data-driven design and envision ways in which future HCI research can develop ways to work with data in the design process in a connected, rich, human manner

Educating about Responsible AI in IS: Designing a course based on Experiential Learning

Responsible AI (RAI) is an emerging topic in the Information Systems (IS) literature. RAI entails ensuring ethical, transparent, and accountable use of AI technologies in line with societal values, expectations, and norms. The challenge for research on IS education at university level is to accompany the growing research on RAI with approaches to educate students about this emerging theme. Research on IS education on responsible AI remains scarce to date, however. We ask: How can we design a course to educate students about responsible AI? We build on earlier research and an experiential learning-based approach to propose a course design promoting students’ multidisciplinary, problem-based learning about RAI applied to the case of public welfare services. Our study is based on participatory observations of student groups and group interviews after a project, acting as an arena where the students could reflect on the learning process and evolving awareness of RAI

A Theoretical Approach Towards Designing InfoVis for Decision Support Effectiveness

t—Information Visualization (InfoVis) as information systems used in gaining insights of large and multidimensional dataset has gained interest of human computer interaction researchers.The researchers have also craved for more theorybased design models to support designing InfoVis and to enhance its decision support effectiveness.This is a result of the observed insufficiency in the theoretical explanation and model of InfoVis design generally, and its decision support effectiveness, specifically.Extant literature reviewed showed that there is lack of studies that explicitly state the linkage between InfoVis design techniques and respective supporting theories, and how this translate to decision support design of InfoVis.This study therefore employs an unobtrusive research method that involves thematic analysis of InfoVis design and related theoretical literatures, to characterize, categorize and link the InfoVis theories with their respective design techniques.The result is a proposed theoretical design model.The model is therefore used, as a validation process, in the design of StudentViz – an InfoVis to support the multidimensionality of students’ dataset

A Theoretical Approach Towards Designing InfoVis for Decision Support Effectiveness

Information Visualization (InfoVis) as information systems used in gaining insights of large and multidimensional dataset has gained interest of human computer interaction researchers. The researchers have also craved for more theorybased design models to support designing InfoVis and to enhance its decision support effectiveness. This is a result of the observed insufficiency in the theoretical explanation and model of InfoVis design generally, and its decision support effectiveness, specifically. Extant literature reviewed showed that there is lack of studies that explicitly state the linkage between InfoVis design techniques and respective supporting theories, and how this translate to decision support design of InfoVis. This study therefore employs an unobtrusive research method that involves thematic analysis of InfoVis design and related theoretical literatures, to characterize, categorize and link the InfoVis theories with their respective design techniques. The result is a proposed theoretical design model. The model is therefore used, as a validation process, in the design of StudentViz – an InfoVis to support the multidimensionality of students’ dataset

ICS Materials. Towards a re-Interpretation of material qualities through interactive, connected, and smart materials.

The domain of materials for design is changing under the influence of an increased technological advancement, miniaturization and democratization. Materials are becoming connected, augmented, computational, interactive, active, responsive, and dynamic. These are ICS Materials, an acronym that stands for Interactive, Connected and Smart. While labs around the world are experimenting with these new materials, there is the need to reflect on their potentials and impact on design. This paper is a first step in this direction: to interpret and describe the qualities of ICS materials, considering their experiential pattern, their expressive sensorial dimension, and their aesthetic of interaction. Through case studies, we analyse and classify these emerging ICS Materials and identified common characteristics, and challenges, e.g. the ability to change over time or their programmability by the designers and users. On that basis, we argue there is the need to reframe and redesign existing models to describe ICS materials, making their qualities emerge

Seeing Through Things:Exploring the Design Space of Privacy-Aware Data-Enabled Objects

Increasing amounts of sensor-augmented research objects have been used in design research. We call these objects Data-Enabled Objects, which can be integrated into daily activities capturing data about people's detailed whereabouts, behaviours, and routines. These objects provide data perspectives on everyday life for contextual design research. However, data-enabled objects are still computational devices with limited privacy awareness and nuanced data sharing. To better design data-enabled objects, we explore privacy design spaces by inviting 18 teams of undergraduate design students to re-design the same type of sensor-enabled home research camera. We developed the Connected Peekaboo Toolkit (CPT) to support the design teams in designing, building, and directly deploying their prototypes in real home studies. We conducted Thematic Analysis to analyze their outcomes which led us to interpret that privacy is not just an obstacle but can be a driver by unfolding an exploration of possible design spaces for data-enabled objects.</p