Elizabeth M. Gerber

People now connect, collaborate, and maintain relationships through technologies far more complex than early computer-mediated communication (CMC). Beyond text, audio, and video, today’s tools include social robots, tangible interfaces, and virtual reality, all of which actively shape routines and relationships. The rapid rise of AI further changes the picture: it now edits, augments, and even generates messages, transforming how people express intentions and emotions. These capabilities promise assistive gains but also raise concerns about authenticity, over-automation, and interaction quality. This workshop invites interdisciplinary researchers and practitioners to explore the opportunities and challenges of integrating AI into communication technologies. We view communication as a layered social practice involving the negotiation of presence and connectedness, attention to partners and contexts, and self-presentation while attuning to others’ emotions. Our goals are to: (1) co-develop design agendas grounded in these practices, (2) identify recurring opportunities and risks of AI integration, and (3) propose sustainable directions that respect autonomy, authenticity, and social well-being. Participants will share experiences and uncover design opportunities through short talks and interactive sessions. Together, we aim to deepen understanding of CMC in the age of AI and reimagine technologies that foster meaningful interaction.

Although knowledge workers are increasingly able to adopt remote and hybrid working arrangements and work productively, many organizations continue to question the effectiveness of remote work and focus on its concerns and challenges. Previous CSCW research shows that remote workers have limited awareness of other workers, require more explicit coordination, and feel excluded from in-person colleagues. Research also shows that adopting work practices and technologies that are remote work-friendly can offset many of these challenges. Identifying which effective practices and challenges are most helpful or hurtful to remote workers-and how workplace attributes (e.g., team structure; communication frequency; tool use) affect them-could strengthen organizations' strategies and policies for remote work. Through a theoretically-informed survey of 1,526 U.S. knowledge workers, we find many factors prior research has argued as essential to remote work, such as knowing your teammates personally, to be the least important for remote workers, and show how workplace attributes influence those perceptions. We provide theoretical and practical implications for future research for organizations that wish to support remote and hybrid work modalities.

Entrepreneurship requires navigating open-ended, ill-defined problems: identifying risks, challenging assumptions, and making strategic decisions under deep uncertainty. Novice founders often struggle with these metacognitive demands, while mentors face limited time and visibility to provide tailored support. We present a human-AI coaching system that combines a domain-specific cognitive model of entrepreneurial risk with a large language model (LLM) to proactively scaffold both novice and mentor thinking. The system proactively poses diagnostic questions that challenge novices' thinking and helps both novices and mentors plan for more focused and emotionally attuned meetings. Critically, mentors can inspect and modify the underlying cognitive model, shaping the logic of the system to reflect their evolving needs. Through an exploratory field deployment, we found that using the system supported novice metacognition, reduced mentors' cognitive load, and improved meeting depth, intentionality, and focus--while also surfaced key tensions around trust, misdiagnosis, and expectations of AI. We contribute design principles for proactive AI systems that scaffold metacognition and human-human collaboration in complex, ill-defined domains, offering implications for similar domains like healthcare, education, and knowledge work.

Organizational researchers increasingly call for applied impact, yet institutional structures continue to privilege theoretical novelty over practical relevance. In contrast, engineering fields have built mechanisms that legitimize rigorously validated, usable contributions—often publishing proven solutions before fully developed theories. Drawing on our experiences in both engineering and organizational research, we examine how institutional design—not just individual motivation—shapes what counts as legitimate scholarship. We identify structural levers that support applied impact across three institutional pillars: cognitive (what counts as knowledge), normative (what confers prestige), and regulative (what gets published and rewarded). By analyzing how engineering disciplines use diverse publication formats, evaluation rubrics, and inclusive authorship norms, we outline feasible reforms for organizational research. We propose a framework for institutional redesign that expands the definition of scholarly value while preserving rigor. History: This manuscript is part of the five-piece crossroads collection "Organization Research as an Applied Science," edited by Gokhan Ertug and Stephen Zhang. The companion pieces are Zhang and Ertug (2025) , Croson and Croson (2025) , Berry (2025) , and Yoeli and Rand (2025) . Funding: E. Gerber gratefully acknowledges funding from the National Science Foundation (NSF). C. Eesley gratefully acknowledges funding from the Stanford Technology Ventures Program (STVP).

Personal narratives are key to developing self-concept which influences how we see and value ourselves.As adolescents who are still developing their self-concept increasingly use generative AI to write personal narratives, our knowledge of how AI constructs personal narratives is limited.Through a mixed-methods algorithmic audit of 160 AI-generated college application essays created in OpenAI ("o1" and "4o"), we find that prompts referencing marginalized gender identities more often yield narratives focused on overcoming societal bias or contributing to in-group communities.We also observe that the newer model sometimes refuses to provide a direct essay, especially when prompted from a first-person perspective.Our content analysis, informed by narrative psychology theory, highlights how these AI responses can both reflect and reinforce prevailing social biases, thereby shaping adolescents' emerging self-concepts.While AI holds promise in democratizing narrative coaching, it also poses risks of perpetuating stereotypes and displacing authentic self-expression.Future design for narrative identity-relevant AI models should focus on reducing reliance on stereotypes by enhancing training data diversity to support identity development and ensuring equitable access to educational features across paid and free models.

This panel symposium aims to revisit our assumptions about organizational structure in light of the ever-increasing digitalization of the world of work and the ways in which design choices underpinning digital technologies inform organizing. Although scholars have continuously updated theories of organizational structure, which first emerged in the context of manufacturing technologies, technological advances have made ordinary what was once exceptional. Digital work technologies enable more fluid, dynamic, or scalable structures; in some work settings, these technologies are synonymous with the organization itself. Thus, it is imperative to examine whether and how interface design heuristics and decisions—imbued with specific values, beliefs, and ideologies—affect organizational structure. Given that the examination of design principles—and actual design—of digital technologies is largely the purview of neighboring fields, the symposium brings together experts in Organization Theory, Technology, and Work with scholars in Design Thinking and Human-Computer Interaction to build connections and generate insights into the impact of digital technologies on the structure of work and organizing.

This panel creates space for exploratory conversation on processually-minded perspectives on designing that address such questions as: --Who are “designers”? Who “does” design? --What is design? --Where does design occur? --When is design used? --Why does design occur; to what end(s)? --How are these design-related activities carried out in an organization?

Our proposed research seeks to design co-writing AI systems to preserve the writer’s personal voice and maintain evaluation integrity with under-resourced high school students applying to college.

This workshop aims to support participants in reflecting, ideating, and prototyping new socio-technical approaches to help workers develop effective collaboration skills for complex work. While CSCW researchers have created tools to provide workers access to collaboration opportunities, workers require more support in learning how to collaborate effectively to benefit from these opportunities. We invite academic and industry researchers who study these topics and develop socio-technical systems for workplaces to participate in this workshop. Participants will share insights from their work and work with each other to envision an agenda for future research and design of workplaces that support learning how to collaborate. Discussion and ideas generated from this workshop will be synthesized and archived online for the larger research community and the general public. We hope these discussions will foster new collaborations and further develop a community of researchers who have supporting learning as an agenda for the future of work.

Abstract Background To create design solutions experienced engineering designers engage in expert iterative practice. Researchers find that students struggle to learn this critical engineering design practice, particularly when tackling real‐world engineering design problems. Purpose/Hypothesis To improve our ability to teach iteration, this study contributes (i) a new teaching approach to improve student teams' expert iterative practices, and (ii) provides support to existing frameworks—chiefly the Design Risk Framework—that predict the key metacognitive processes we should support to help students to engage in expert iterative practices in real‐world engineering design. Design/Method In a 3‐year design‐based research study, we developed a novel approach to teaching students to take on real‐world engineering design projects with real clients, users, and contexts to engage in expert iterative practices. Results Study 1 confirms that student teams struggle to engage in expert iterative practices, even when supported by problem‐based learning (PBL) coaching. Study 2 tests our novel approach, Planning‐to‐Iterate, which uses (i) templates, (ii) guiding questions to help students to define problem and solution elements, and (iii) risk checklists to help student teams to identify risks. We found that student teams using Planning‐to‐Iterate engaged in more expert iterative practices while receiving less PBL coaching. Conclusions This work empirically tests a design argument—a theory for a novel teaching approach—that augments PBL coaching and helps students to identify risks and engage in expert iterative practices in engineering design projects.

The number of hackathon events worldwide has nearly quadrupled in the last five years. Despite exponential growth across diverse industries and increasing interest across academic disciplines, our integrated understanding of the phenomena of hackathons is limited. We conduct the first multidisciplinary literature review of publications from 1999 to 2022 to understand the conceptualization of the phenomena over time. We find that hackathon research can be categorized into 4 core areas (purpose, format, processes, and outcomes). Research was first driven by a purpose (innovation, learning, and collaboration), followed by an examination of how formats adjust to purpose to influence what happens (processes) and what is produced (outcomes), and critical reviews of the hackathon phenomena. We contribute a unifying framework with these four core areas to inform future directions of hackathon research and practice, as well as a discussion of the need for longitudinal and multidisciplinary research of hackathons.

One-to-many coaching is a common, yet difficult, coaching technique used in environments with many novices learning to solve ill-defined problems. Intelligent systems might be designed to support 1-to-many coaching but designing such systems requires a 1-to-many coaching model that details novices' challenges, coaches' strategies, and coaches' goals. To build such a model, we conducted interaction analysis on 24 1-to-many coaching sessions with novices developing new products in a university incubator and conducted retrospective analyses with 3 coaches and 30 novices. We contribute a model that demonstrates that coaches in a 1-to-many setting not only need to help novices develop metacognitive skills (just as in 1-to-1 coaching), but also need to utilize the presence and expertise of a group of novices to learn from each other, to mitigate their fear of failures, and provide them accountability. Our model informs design implications for future intelligent coaching systems to (1) assist coaches in monitoring and comparing many novices' progress, learning, and expertise; (2) provide novices with checklists, templates, and scaffolds to help them self-evaluate, seek-help, and summarize learning; (3) showcase failures and growth; and (4) publicize planning and progress to provide accountability.