SIGs & Projects

Today’s work and learning communities have become increasingly networked through the inclusion of a variety of social support structures, agile working processes, and productivity tools that facilitate their members in solving complex, ill-structured problems. While these advances are necessary for supporting complex work, learning how to connect one’s working process with the support opportunities throughout the ecosystem can be challenging for novices since it requires them to continually monitor for their needs, plan strategies to resolve them, and replan as progress on their needs is made. Towards addressing these challenges, this SIG develops technologies that help people develop orchestration strategies for working and learning across the support opportunities present in these complex organizations.

Breaking Boundaries focuses on exploring novel interactions and connecting people in unusual and unexpected ways. The goal of this SIG is to redefine the boundaries of what is possible in our everyday interactions and ways of doing. Example projects aim to empower interactions that (a) share real-world collective experiences as readily as we would share information via social media today; (b) provide personalized action plans for novice web developers as they embark on their first personal project; (c) allow developers to prototype mobile apps in low fidelity, but still test them outside of the lab in realistic scenarios; (d) blur the lines between physical and virtual reality; and (e) connects strangers through sharing of secret information about their community.

Learning how to program is hard, and many students in introductory computer science courses struggle to overcome programming challenges on their own. These students are unsatisfied with their performance on assignments, but don’t know how to identify ineffective actions in their process, reflect on the root causes for these actions (ex: impostor syndrome, low self-efficacy, low sense of belonging), or recognize potential obstacles. Without adopting solutions that address ineffective actions, their root causes, and obstacles, they will continue to struggle in CS. 

How can we imbue machines with a deeper understanding of human situations and contexts, and the interactions they afford? Despite significant advances in machine sensing and machine learning technologies—and the availability of rich APIs for creating AI-powered applications—it remains difficult for designers to express their concept of a human situation (e.g., places to hold a private conversation; good places to take a bike ride with young children) to machines so that applications can be aware and responsive to the situation across a variety of distributed contexts. Failing to do so could result in costly errors, violate safety and privacy, or lead to inequitable access to AI supported experiences. To fill this gap, this SIG's projects advance new human-AI expression tools that support designers using their understanding of human situations to construct machine representations using available context features. 

Undergraduate research experiences enhance learning and professional development, but providing effective and scalable research training is often limited by practical implementation and orchestration challenges. Agile Research Studios (ARS) is a new socio-technical model for expanding research training opportunities that supports research communities of practice without increasing faculty mentoring resources. ARS integrates and advances professional best practices and organizational designs, principles for forming effective learning communities, and design of social technologies to overcome the orchestration challenge of one faculty researcher mentoring 20 or more students. The DTR program uses the ARS model to improve the quality of learning, produce research outcomes, and lower the barrier to participation while increasing the number of students who receive authentic research training.

Experiential computing is about building computational systems that can reason about the social, cultural, physical, and practical realities that shape how people actually experience activities in different contexts, instead of only using low-level signals like place category or location. In this grant, the research focuses on developing LLM-based and data-driven models, grounding methods, and human-AI interfaces/workflows that help designers understand how an experience may differ across user groups, geographies, and settings, and then use that understanding to build more robust human-centered computing systems.

Our current interaction with technology is focused only on the output that the computer can deliver, because HCI language and logics are all based on consequentialist beliefs. This focus on immediate output ignores the essential questions of why and how, threatening human capacity for reflective engagement. If HCI continues without questioning its underlying output-focused logic, we risk diminishing the very qualities that make us human.