Service Design in HCI Research: The Extended Value Co-creation Model

In this paper, we discuss what it means to practice service design in an academic research setting. For a long time, the primary focal point of design research has been the users—of their experiences, needs, desires, and values. By contrast, designers have been relatively anonymous and unlocatable. In shift to the service-centric design paradigm, we argue that it is important to recognize design researchers as distinct stakeholders, who actively interact with systems and services with a goal to fulfill their own values and achieve desired outcomes. In practice, typically the role of designer is that of a design consultant working for (or rather on behalf of) the client. By contrast, in academic research settings, the role of designer is that of a design researcher working with their own research agenda.We provide a case study of a service design research project aimed at developing new digital services for public libraries. We encountered a series of issues with a complex set of values at play, in which design researchers emerged as distinct stakeholders with specific sets of research questions, goals, and visions. The main contribution of this paper is a model that (a) clarifies the position of design researchers within the sociocultural context in which they practice design, and (b) visualize how their positions impact the value co-creation, and in turn, the design outcome

Continuous improvement: How systems design can benefit the data-driven design community

Introduction Currently, the learning science community is exploring the use of data-driven design to improve K12 educational systems. These “continuous-improvement systems” aim to align strategic goals, outcome metrics and human-computer system processes to support improved learning outcomes. However, the learning science community has only begun to apply systemic design to practical implementation of these systems. In this paper, we present several examples of data-driven design in K12 educational systems in order to identify aspects that can benefit from systemic design. Through these case studies, we focus on three concepts: 1) systemic designers can ensure that the system is capable of measuring successful outcomes; 2) systemic designers can ensure that system optimization will improve intended outcomes while minimizing unintended consequences; and 3) systemic designers can portray what a future with these continuous improvement systems will be like to the educational community, before any resources are committed to building the technology. Example #1: Ensure that the system is capable of measuring successful outcomes Data can be used to inform system stakeholders about the success of designed systems; that is, how well outcome measures align with system intentions. For instance, after providing an instructional activity (lecture, small group, video, etc) in class, a teacher might assign their students an “exit ticket” quiz to assess whether the instructional activity was successful. These quizzes support data-driven decisions about how to spend time and effort in the classroom. Variations in student performance give teachers an understanding of the students who need greater attention and the learning objectives that need greater attention. Further, digital data from exit tickets or other formative assessments can be aggregated across teachers to provide school administrators with continuous insight into the areas of need, such as students or teachers who need additional help or learning objectives that are posing special challenges. Providers of digital instruction can then aggregate usage and performance across many schools in order to identify successful and unsuccessful usage patterns. Data-driven continuous improvement can occur at multiple levels (i.e., teacher, school & software provider) when systems are designed to generate valid outcome metrics of success (goal achievement). Example #2: Ensure that system optimization will improve intended outcomes while minimizing unintended consequence Success metrics can be used by human teams and AI systems to drive continuous improvement. However, the optimization of metrics can produce unintended consequences when chosen metrics are not fully aligned to intended outcomes and when feedback loops about metric suitability are impoverished. In this case study, an online educational game is designed with the goal of motivating students to practice math problems. After being deployed online, the game attracts several thousand students a day; these players are randomly assigned to different game design variations to observe how the effects of different designs on key outcome metrics (e.g., duration of voluntary play). To investigate the role of AI in system design optimization, we implemented a UCB multi-armed bandit (a reinforcement learning AL/ML algorithm) to automatically test variations in the existing game parameter space (e.g., time limits, etc). The algorithm is designed to optimally balance the exploration of potential game designs with exploitation of the most successful designs; sometimes it will randomly search the game design space for configurations that maximize metrics (duration of voluntary play time) and sometimes it will deploy the most successful variations. While the algorithm worked as intended, the system “spun out of control” and primarily deployed malformed game designs that were maximizing the outcome metric but were misaligned with the original educational intent: the game variations were likely played for long periods of time because they were absurdly easy. This shows the pitfalls of having AI systems engage in automatic optimization without humans in the loop as a governing feedback system. Systemic designers need to design feedback systems to monitor system AI to ensure that outputs are meaningfully aligned to system intentions. Example #3: Portray what the future will be like Artificial intelligence has the potential to facilitate the work of teachers by reducing the effort required to use data to inform personalized instruction. However, AI can be intimidating or off-putting to teachers who do not understand its operation or intentions. In this case study, we deployed a teacher-facing recommendation system that uses reinforcement learning to continuously improve recommendation usefulness to teachers. To design a reinforcement learning AI system, there must be data representations of the system state, the space of possible actions and a reward signal tied to a success metric. In our case, the system state is student digital performance on learning activities, the action possibilities are the different digital items that teachers can next assign to a student and the reward signal occurs when teachers act upon a recommendation (i.e., when they assign those digital activities recommended by the system). This system embodies two key elements that diverge from most existing work in “adaptive learning” or “intelligent tutoring systems.” First, the system emphasizes human-technology teamwork, in contrast to human replacement, so that teachers are empowered by the assistance of the AI. Secondly, the artificial intelligence is deliberately constructed as an aggregation of human intelligence: the system learns from the activity-assignment decisions that are made by thousands of other human teachers and aggregates them into artificially intelligent recommendations. To promote adoption of this system, a key role for systemic design is making the intended future vision accessible and attractive to teachers and other stakeholders. Systemic designers can help to engage humans to participate in the decision making by presenting a glimpse of what a data-driven future might be like in the classroom. Conclusion Across these case studies, we show how systemic design can aid diverse participants in the implementation of data-driven design and optimization. Systemic design insight can contribute to the negotiation of meaningful and robust metrics of success, to the construction of human-in-the-loop governance of AI systems and to the representation of potential futures. We expect designers to play a crucial role in taming the complexity of practical AI-human systems and aligning system outcomes to sustainable, humanistic values

Проблеми підготовки майбутніх учителів до розвитку математичних здібностей у старшокласників

For elders who remain independent in their homes, the home becomes more than just a place to eat and sleep. The home becomes a place where people care for each other, and it gradually subsumes all activities. This article reports on an ethnographic study of aging adults who live independently in their homes. Seventeen elders aged 60 through 90 were interviewed and observed in their homes in 2 Midwestern cities. The goal is to understand how robotic products might assist these people, helping them to stay independent and active longer. The experience of aging is described as an ecology of aging made up of people, products, and activities taking place in a local environment of the home and the surrounding community. In this environment, product successes and failures often have a dramatic impact on the ecology, throwing off a delicate balance. Jodi Forlizzi is an interaction designer with an interest in the intersection of aesthetic, assistive, and social products; she is an Assistant Professor in the Human-Computer Interaction Institute and the School of Design at Carnegie Mellon University. Carl DiSalvo is a designer with an interest in the relation among agency, the body, power, and design; he is a PhD candidate in th

A fieldwork of the future with user enactments

Designing radically new technology systems that people will want to use is complex. Design teams must draw on knowledge related to people’s current values and desires to envision a preferred yet plausible future. However, the introduction of new technology can shape people’s values and practices, and what-we-know-now about them does not always translate to an effective guess of what the future could, or should, be. New products and systems typically exist outside of current understandings of technology and use paradigms; they often have few interaction and social conventions to guide the design process, making efforts to pursue them complex and risky. User Enactments (UEs) have been developed as a design approach that aids design teams in more successfully investigate radical alterations to technologies ’ roles, forms, and behaviors in uncharted design spaces. In this paper, we reflect on our repeated use of UE over the past five years to unpack lessons learned and further specify how and when to use it. We conclude with a reflection on how UE can function as a boundary object and implications for future work