Theory of Mind (ToM) -- the ability to infer what others are thinking (e.g., intentions) from observable cues -- is traditionally considered fundamental to human social interactions. This has sparked growing efforts in building and benchmarking AI's ToM capability, yet little is known about how such capability could translate into the design and experience of everyday user-facing AI products and services. We conducted 13 co-design sessions with 26 U.S.-based AI practitioners to envision, reflect, and distill design recommendations for ToM-enabled everyday AI products and services that are both future-looking and grounded in the realities of AI design and development practices. Analysis revealed three interrelated design recommendations: ToM-enabled AI should 1) be situated in the social context that shape users' mental states, 2) be responsive to the dynamic nature of mental states, and 3) be attuned to subjective individual differences. We surface design tensions within each recommendation that reveal a broader gap between practitioners' envisioned futures of ToM-enabled AI and the realities of current AI design and development practices. These findings point toward the need to move beyond static, inference-driven approach to ToM and toward designing ToM as a pervasive capability that supports continuous human-AI interaction loops.

Technology companies continue to invest in efforts to incorporate responsibility in their Artificial Intelligence (AI) advancements, while efforts to audit and regulate AI systems expand. This shift towards Responsible AI (RAI) in the tech industry necessitates new practices and adaptations to roles—undertaken by a variety of practitioners in more or less formal positions, many of whom focus on the user-centered aspects of AI. To better understand practices at the intersection of user experience (UX) and RAI, we conducted an interview study with industrial UX practitioners and RAI subject matter experts, both of whom are actively involved in addressing RAI concerns throughout the early design and development of new AI-based prototypes, demos, and products, at a large technology company. Many of the specific practices and their associated challenges have yet to be surfaced in the literature, and distilling them offers a critical view into how practitioners’ roles are adapting to meet present-day RAI challenges. We present and discuss three emerging practices in which RAI is being enacted and reified in UX practitioners’ everyday work. We conclude by arguing that the emerging practices, goals, and types of expertise that surfaced in our study point to an evolution in praxis, with associated challenges that suggest important areas for further research in HCI.

Due to the lack of face-to-face interactions, online learners frequently experience social isolation that negatively impacts students’ well-being and learning experiences. Many text-based AI agents have been equipped with different social characteristics and functionalities to support people who are socially isolated. However, the design of agent’s functionalities, social characteristics, and ethical challenges in promoting social connectedness among online learners are underexplored. Taking a co-design approach, we included 23 online learners enrolled in an online for-degree graduate program as active participants in two virtual co-design workshop studies. Through four different co-design activities, we identified online learners’ preferences for AI agent’s functionalities and social characteristics in promoting their social connectedness as well as potential ethical concerns. Based on our findings, we establish the role of AI agent as a facilitator to continuously scaffold online learners’ social connection process. We further discuss the unique ethical challenges regarding agent-mediated social interaction in online learning.

Our online interactions are constantly mediated through Artificial Intelligence (AI), especially our social interactions. AI-mediated social interaction is the AI-facilitated process of building and maintaining social connections between individuals through information inferred from people's online posts. With its impending application across a number of contexts, the challenges and opportunities of AI-mediated social interaction remain underexplored. This paper seeks to understand the design space of AI-mediated social interaction in the context of online learning, where students frequently face social isolation. We deployed an AI agent named SAMI in three class discussion forums to help online learners build social connections. Using SAMI as a probe, we conducted semi-structured interviews with 26 students to understand their difficulties in remote social interactions and their experiences with SAMI. Through the lenses of social translucence and social-technical gap, we illustrate online learners' difficulties in remote social interactions and how SAMI resolved some of the difficulties. We also identify potential ethical and social challenges of SAMI such as user agency and privacy. Based on our findings, we outline the design space of AI-mediated social interaction. We discuss the design tension between AI performance and ethical design and pinpoint two design opportunities for AI-mediated social interaction in designing towards human-AI collaborative social matching and artificial serendipity.

Building conversational agents that can conduct natural and prolonged conversations has been a major technical and design challenge, especially for community-facing conversational agents. We posit Mutual Theory of Mind as a theoretical framework to design for natural long-term human-AI interactions. From this perspective, we explore a community’s perception of a question-answering conversational agent through self-reported surveys and computational linguistic approach in the context of online education. We first examine long-term temporal changes in students’ perception of Jill Watson (JW), a virtual teaching assistant deployed in an online class discussion forum. We then explore the feasibility of inferring students’ perceptions of JW through linguistic features extracted from student-JW dialogues. We find that students’ perception of JW’s anthropomorphism and intelligence changed significantly over time. Regression analyses reveal that linguistic verbosity, readability, sentiment, diversity, and adaptability reflect student perception of JW. We discuss implications for building adaptive community-facing conversational agents as long-term companions and designing towards Mutual Theory of Mind in human-AI interaction.

As online education proliferates, one concern that has been raised is that it may fail to capture desirable emergent phe-nomena from on-campus programs. Student community is one example of such a phenomenon: on-campus student communities thrive based on synchronous collocation. An online program might be designed to capture all deliberate constructs in an on-campus program, but there may be beneficial side effects of synchronous collocation that are not apparent. In this work, we examine the issue of social isolation in an online graduate program. By happenstance, three studies were conducted in relative isolation looking at social isolation from different angles. The first study exam-ined trajectories in social presence as a semester proceeded. The second study developed an understanding of students' needs with regard to community in an online program. The third study tested out an immersive virtual environment to try to improve students' sense of connectedness. Combin-ing their findings, we find compelling evidence of the exist-ence of a Synchronicity Paradox in online education: stu-dents desire synchronicity to form strong social communi-ties, and yet part of the chief appeal of these online pro-grams is their asynchronicity. In light of this finding, we provide design guidelines for how synchronicity may be reintroduced into asynchronous programs without sacrific-ing the benefits of asynchronicity. More specifically, we propose that scale itself may be the key to building emer-gent synchronicity.

Large-scale educational settings have been common domains for affect detection and recognition research. Most research emphasizes improvements in the accuracy of affect measurement to enhance instructors' efficiency in managing large numbers of students. However, these technologies are not designed from students' perspectives, nor designed for students' own usage. To identify the unique design considerations for affect sensors that consider student capacities and challenges, and explore the potential of affect sensors to support students' self-learning, we conducted semi-structured interviews and surveys with both online students and on-campus students enrolled in large in-person classes. Drawing on these studies we: (a) propose using affect data to support students' self-regulated learning behaviors through a "scaling for empowerment'' design perspective, (b) identify design guidelines to mitigate students' concerns regarding the use of affect data at scale, (c) provide design recommendations for the physical design of affect sensors for large educational settings.

Interaction design is playing an increasingly prominent role in computing research, while professional user experience roles expand. These forces drive the demand for more de- sign instruction in HCI classrooms. In this paper, we distill the popular approaches to teaching design to undergraduate and graduate students of HCI. Through a review of existing research on design pedagogy, an international survey of 61 HCI educators, and an analysis of popular textbooks, we ex- plore the prominent disciplinary perspectives that shape design education in the HCI classroom. We draw on our analyses to discuss the differences we see in forms of design taught, approaches to adapting design instruction in computing-based courses, and the tensions faced by instructors of these classes. We conclude by arguing for the importance of pedagogical research on design instruction as a vital and foundational area of inquiry in Interaction Design and HCI.