Narratives of learning often implicitly rely on linear temporalities, such as understanding time through a clock or positioning learning as a trajectory. When designing technology-rich learning environments, we need attention to the multiple times and temporalities that exist. In this paper, we expand on a temporal vocabulary [27] to conceptualize how we may attend to and design for alternative temporalities for understanding learning in technology-rich learning environments. We conceptualize undiscoverable learning [39] as a generative orientation for configuring non-linear time(s). To develop this combination, we first describe a 3-year partnership for the design of technology-rich learning environments with a public library in the southwestern US through encounters and other-time. We then offer three original terms that help expand our understanding of temporality in design for learning: traces, stamps, and renderings. The implications of this work can be seen across the intersections of learning, HCI, and design.

Peer Reviewed

OBJECTIVES: To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe. MATERIALS AND METHODS: This prospective quality improvement study was conducted at a large academic medical center with 45 physicians from 8 ambulatory disciplines over 3 months. Utilization and documentation times were derived from electronic health record (EHR) use measures. RESULTS: The ambient AI scribe was utilized in 9629 of 17 428 encounters (55.25%) with significant interuser heterogeneity. Compared to baseline, median time per note reduced significantly by 0.57 minutes. Median daily documentation, afterhours, and total EHR time also decreased significantly by 6.89, 5.17, and 19.95 minutes/day, respectively. DISCUSSION: An early pilot of an ambient AI scribe demonstrated robust utilization and reduced time spent on documentation and in the EHR. There was notable individual-level heterogeneity. CONCLUSION: Large language model-powered ambient AI scribes may reduce documentation burden. Further studies are needed to identify which users benefit most from current technology and how future iterations can support a broader audience.

OBJECTIVE: This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout. MATERIALS AND METHODS: This prospective quality improvement study was conducted at Stanford Health Care with 48 physicians over a 3-month period. Outcome measures included burden, burnout, usability, and perceived time savings. RESULTS: Paired survey analysis (n = 38) revealed large statistically significant reductions in task load (-24.42, p <.001) and burnout (-1.94, p <.001), and moderate statistically significant improvements in usability scores (+10.9, p <.001). Post-survey responses (n = 46) indicated favorable utility with improved perceptions of efficiency, documentation quality, and ease of use. DISCUSSION: In one of the first pilot implementations of ambient AI scribe technology, improvements in physician task load, burnout, and usability were demonstrated. CONCLUSION: Ambient AI scribes like DAX Copilot may enhance clinical workflows. Further research is needed to optimize widespread implementation and evaluate long-term impacts.

This study offers a critical analysis of how the Indian gig platform, Urban Company (UC), mobilizes the promise of empowerment to assemble, discipline and algorithmically entangle its gig labor force. Much of the scholarship on gig work has described how the (mis)classification of gig workers as independent contractors allows gig corporations to abdicate their employment responsibilities, thereby disentangling the traditional employer-employee relationship. By contrast, UC promises to invest in it's Indian gig workforce, with offerings such as health insurance and financial loans to counter the enduring socio-economic precarity of the Indian labor market. However, through a critical content analysis of UCs' public media, we find that UC's organization of such ''social security'' benefits rests on an insidious system of algorithmic evaluation and classification. Access to benefits is conditional, determined by the platform's algorithmic control mechanisms, which classify workers as worthy or unworthy on a shifting basis. Mobilizing Agre's analysis of business discourses of ''empowered work'', we highlight how UC's algorithmic classification of workers creates a system of ''conditional empowerment.'' This system reflects a critical shift in the labor process, from rights-based labor regimes to a neoliberal social order where individuals must constantly prove their worth.

Homelessness is a significant and growing crisis in the United States. In an effort to more efficiently and fairly distribute limited housing resources, jurisdictions across the US have adopted algorithmic prioritization systems to help select which unhoused people should receive resources. Given the impact of algorithmic prioritization on the lives of unhoused people, there is a need to more fully examine how these systems are implemented in practice by frontline workers such as social service workers. In this paper, we present a qualitative study that draws on interviews and artifact walkthroughs with fifteen social service workers to examine how they interacted with algorithmic prioritization systems as part of their job duties. We found that social service workers employed discretionary work practices to mediate between the rigid formats of algorithmic prioritization systems and the messy, situated realities of homelessness. We term these discretionary work practices 'intermediation' and provide four examples that illustrate how our interlocutors were able to maintain their commitment to advocacy and to express their expertise despite the automation of major aspects of their professional decision-making. These work practices, which we argue were motivated by care for clients, as well as a desire to preserve professional autonomy, lead us to conclude that discretion cannot easily be removed from bureaucratic systems and that removing discretion is not necessarily a desirable outcome.

We examine how two prominent food delivery platforms in India, Swiggy and Zomato, produce a managed digital workforce using a combination of algorithmic control and traditional labor management strategies. Our findings draw from interviews conducted with 13 food delivery workers and a critical discourse analysis of news media coverage. We found that the two platforms combine piece wage restructuring, granular datafication practices, and the use of benevolent language as neoliberal social control mechanisms. We find that this combination of technological governance and strategic managerial practices is a mutually constitutive method of control that restructures labor processes, extracts workers’ compliance and consent, and prevents work disruption. We show that contemporary platform companies draw from strategies that have historically been deployed in industrial labor management. By examining how older and newer regimes of social control and exploitation are strategically intertwined in contemporary platform design, we contribute a historically situated understanding of platform labor that moves beyond dualistic interpretations of “traditional” labor management practices and more recent algorithmic modes of control. Our findings contribute to recent debates in tech labor and algorithmic control by examining how contemporary conditions of precarious work reactivate certain past forms of control and in doing so normalize extreme overwork, exhaustion, speedups, and injuries.

Norms and values in computing education are constantly changing as dominant narratives about the role of computing in society evolve over time. Within the current evolving landscape of computing education, researchers and practitioners have advocated for ensuring people from all backgrounds, and particularly women, non-binary, and Black, Indigenous, and Latinx people, are able to participate equitably within the field of computing. Yet, the values of computing educational experiences are narrowly framed within career outcomes, such as securing a career in computing, leaving many important experiences and ways of participating in the field out of the picture. To address this, we conducted reflective interviews with women who participated in broadening participation in computing (BPC) programs to understand their perceptions of computing and how it aligns (or not) with what they value about their experiences in computing learning environments. We investigate the following research questions: (1) How do women who participated in BPC programs describe their perceptions of computing? (2) How do those perceptions align or misalign with the program outcomes they valued? The findings from our study call attention to tensions arising from centering “computing careers” in BPC work and highlight the outcomes of participation valued by the women in our study, such as developing communities and relationships, gaining communication skills, and expanding perspectives on skills computer scientists should possess.

Sisters Rise Up is a near-peer mentoring program that attempts to help women of color (Black, Latina, and/or Native American) succeed in their Advanced Placement (AP) Computer Science (CS) courses and on the exams. Women of color are underrepresented in computing careers and have lower pass rates on the AP CS exams than their White and Asian peers. AP Computer Science A (CSA) is equivalent to a college-level first course for computer science majors while AP Computer Science Principles (CSP) is equivalent to an introductory course for non-majors. Secondary students (ages 14-19) take AP courses for college credit and/or placement. Sisters Rise Up offers remote help sessions run by near-peer undergraduate students and uses free and interactive ebooks. Sisters Rise Up originated at the Georgia Institute of Technology in 2014. This paper reports on an effort to scale Sisters Rise Up to six institutions during the 2019-2020 academic year as well as an effort to support women of color through a single institution during 2020-2021. In 2019-2020 there were problems with getting Institutional Review Board (IRB) approval, recruiting, training, and program fidelity. However, at least two of the six institutions had a high pass rate on the AP CS exams in 2019-2020. In 2019-2020 there were 17 women of color enrolled from five states, while in 2020-2021 Sisters Rise Up had 19 women of color enrolled from 12 states. This paper reviews the history, outlines the program details and rationale, presents the outcomes, and outlines future directions.

Critical refusal is an active process; an informed practice of investigating power differences in order to generate more just and equitable alternatives to the status quo. In this paper, we examine what it means to utilize critical refusal as a tool for investigating unequal power dynamics that are produced and reified by data practices. We illustrate the generative capacity of critical refusal by drawing on declarations from The Feminist Data Manifest-No to examine data practices across three real-world cases. By pairing a conceptual exploration of critical refusal with real-world examples, we make a theoretical contribution that is grounded in concrete approaches for generating alternative data practices in ways that account for interlocking struggles across contexts and communities.

Emotional validation describes when one believes that their activities, emotions, beliefs, or other reactions are relevant and meaningful given the circumstance. When people experience distressing, stigmatizing life events, their state of emotional validation and thus their perceived sense of normalcy is often disrupted. Online spaces offer opportunities for coping, managing, and making sense of distress and stigma. In this paper, we focus on pregnancy loss as the context of inquiry and as an important example of a disruptive experience that is also associated with stigma. We examine how online spaces help facilitate or disrupt the process of achieving emotional validation among pregnancy loss survivors. We conducted in-depth interviews with women in the United States who had recently experienced a pregnancy loss. We found that individuals seeking a sense of perceived normalcy after pregnancy loss engage in two forms of validation processes that result in emotional validation - informational and experiential. We identified encounters that disrupt the process of seeking, achieving, and maintaining emotional validation related to: information, designs, algorithms, and interpersonal interactions. We introduce the concept of algorithmic symbolic annihilation to describe the representational and emotional harm participants experienced when they felt they were targets of algorithms assuming that all desired pregnancies proceed as expected. Algorithmic symbolic annihilation refers to how algorithms perpetuate normative and stereotypical narratives about phenomena, where what they account for has power and authority, and what they do not account for does not. To aid in seeking, achieving and sustaining emotional validation among pregnancy loss survivors, we suggest designing for 1) representational belonging to combat symbolic annihilation and 2) information avoidance.

Addressing harm and ethical dilemmas that arise from the design of socio-technical systems requires methodologies that foster critical investigations of normative values, perspectives, and experiences. In this paper, we propose duoethnography as a feminist methodological tool for collaboratively researching interactions between users, devices, and data. Drawing on an interdisciplinary body of work and reflexive methodological traditions, we propose and describe four facets of the methodology: relationality, difference, dialogic process, and critical subjectivity. Next, we provide examples of the methodology "in practice" by detailing how the facets were implemented in a six-month diary study of digital health tracking. We use the study findings to illustrate how the application of these four facets facilitated a collaborative and dialogic process that enhanced self-knowledge and resulted in a multifaceted understanding of individual and shared health experiences. Finally, we conclude by discussing the methodological contributions of duoethnography for socio-technical research critically examining normative values and ethics in design. We contend that the collaborative intentionality of duoethnography offers a unique contribution and unifying methodology that views the personal as a valuable site of knowledge production, positions knowledge formation as a dialogic process, and promotes alternative ways of knowing and meaning making.

Participation is today central to many kinds of research and design practice in information studies and beyond. From user‐generated content to crowdsourcing to peer production to fan fiction to citizen science, the concept remains both unexamined and heterogeneous in its definition. Intuitions about participation are confirmed by some examples, but scandalized by others, and it is difficult to pinpoint why participation seems to be robust in some cases and partial in others. In this paper we offer an empirically based, comparative analysis of participation that demonstrates its multidimensionality and provides a framework that allows clear distinctions and better analyses of the role of participation. We derive 7 dimensions of participations from the literature on participation and exemplify those dimensions using a set of 102 cases of contemporary participation that include uses of the Internet and new media.