Teaching assistants (TAs) play a critical role in computing and HCI education, yet little is known about how they perceive and use AI tools or imagine their future pedagogical uses. We report on a series of design workshops with 131 computing (CS) TAs across two U.S. universities. These workshops invited TAs to reflect on current AI use and envision future AI-enhanced tools and practices. Drawing on surveys and design artifacts, we (1) develop a cross-institutional typology of situated TA uses of AI, revealing opportunities and tensions; (2) show how TAs’ visions of AI are shaped by disciplinary norms, institutional structures, and their intermediary position as student-instructors; and (3) reveal ethical dilemmas. Our findings contribute to HCI by positioning TAs as AI-supported knowledge workers in the education domain; illustrating how design and speculation are shaped by people’s situated understandings of AI and their institutional contexts; and identifying a core tension in which TAs simultaneously preserve and erode the human dimensions of their work, with implications for future instructional tools and human–AI collaboration.

Teaching assistants (TAs) play an increasingly vital role in computer science (CS) education, particularly amid rising enrollments, expanding instructional modalities, and the emergence of generative AI tools. In this evolving landscape, CS TAs are taking on greater responsibilities and often serve as the primary point of personal interaction for students, particularly through recitations, lab sessions, and office hours. However, many CS TAs receive limited preparation in inclusive and responsive teaching practices, limiting their ability to effectively support students from diverse cultural and educational backgrounds. To address this gap, we developed and delivered a series of responsive pedagogy workshops at two diverse institutions. These workshops aimed to deepen CS TAs' understanding of inclusive and responsive teaching strategies, support their implementation in practice, and create space for co-design by positioning TAs not only as learners, but as partners in imagining how responsive pedagogy principles could be more effectively integrated into the courses and contexts in which they teach. In this experience report, we describe the design and implementation of these workshops with 117 TA participants, share workshop materials for broader adoption, and reflect on key findings related to integrating responsive pedagogy into CS education through TA training.

Recently, there has been a growing interest in the role of teaching assistants (TAs) in computer science (CS). This interest is due to the vital role CS TAs play in supporting student learning and their expanding responsibilities driven by growing enrollments in CS programs worldwide. While much of this research focuses on the technical and pedagogical aspects of CS TAs' duties, researchers recognize the need to further explore the unique value human CS TAs provide, particularly with the rise of AI tools and assistants. In this paper, we use qualitative methods to analyze 109 survey responses collected across two different institutions in the United States as part of a larger design-based research project to make two contributions. First, we illustrate how CS TAs adopt humanistic stances and demonstrate care in their roles, thereby expanding prevailing understandings of CS TAs. Second, we detail similarities and differences across CS TAs' experiences at each institution that underscore the importance of understanding CS TAs as they are situated in different institutional contexts. We conclude by discussing implications of this work for computing instruction and TA training, emphasizing the importance of foregrounding the roles and values brought by TAs.

Computing education is often confined to the context of formal education or after-school programs; however, there is a growing industry built around adult education, including workshops, coding intensives, online learning, and apprenticeship programs. Amidst these efforts, little research has explored the workplace as a site for novice adult learners to develop computing skills. In this experience report, we present an integrated training curriculum for adults at DataWorks, an organization that trains and employs novice adults from groups historically underrepresented in computing who seek to advance their career through on-the-job learning. ''Data Fellows'' are hired to complete client projects by providing data services for local organizations, nonprofits, and businesses. Training is integrated into employees' weekly responsibilities at DataWorks, and the curriculum consists of four modules: Microsoft Excel, Critical Data Literacy, Python Fundamentals, and Career Development. In this report, we reflect holistically on the evolution of the curriculum over three years. We distill our reflection into insights to inform other integrated training programs that aim to equip novice adults with computing skills in the workplace.

In this paper, we describe and reflect upon the development of critical consciousness and workplace democracy within an experimental workplace called DataWorks. Through DataWorks, we hire adults from communities historically minoritized in computing education and data careers, and train them in entry-level data skills developed through work on client projects. In this process, workers gain a range of skills. Some of these skills are technical, such as programming for data analysis; some are managerial, such as scoping and bidding projects; others are social, perhaps even political, such as the ability to say "No" to projects. In what follows, we describe a workshop series developed to build the workers' critical literacy and consciousness about their data work, specifically regarding the use of data in machine learning systems. After that, we describe a data project the workers questioned and resisted because they determined the work to be harmful. In that process, they demonstrated and enacted a critical consciousness towards data and machine learning. Reflecting on this enactment of data-focused critical consciousness, we identify themes that characterize a democratic workplace, describe the work of designing for organizational action and institutional relations, and discuss how worker and researcher positionality affects this work. In doing so, we argue for enabling workers to resist and refuse harmful data work and challenge the standard power structures of academic research and data work.

The HCI research community has long centered ethics in HCI research and practice. This interest has persisted as scholars highlight the need for more situated understandings and deeper integration of ethics into HCI. In parallel, HCI scholars and students have become increasingly involved in teaching computing ethics across many different university contexts, bringing in valuable perspectives informed by the connections between HCI and the socio-technical subject matter of computing ethics. Yet explicitly bringing these two threads together – examining the teaching of ethics through an HCI research lens – remains nascent. This paper integrates work in HCI and computing education to focus on the role and experience of computing ethics teaching assistants (CETAs), who are increasingly involved in ethics instruction and whose perspectives are predominantly missing in existing literature spanning HCI and computing education. Drawing on HCI theories and methods, our qualitative study of eleven CETAs at two American universities makes three contributions to the HCI literature. First, we build an understanding of who these TAs are with respect to the unique position of teaching computing ethics. Second, we characterize how CETAs’ teaching and learning is situated and shaped within different communities and institutional contexts. Finally, we suggest several implications for the design of ethics instruction within undergraduate computing programs. More broadly, our work can be viewed as a call to action, encouraging HCI scholars to play a more significant role in studying and designing the teaching and learning of computing ethics.

For decades, determining how to teach computing ethics effectively to undergraduate students has been a major concern. As more universities integrate computing ethics into their curriculum, or seek to further refine existing content, who is involved in teaching computing ethics has come to include both graduate and undergraduate teaching assistants (TAs). However, the role of TAs in ethics education is not well-understood, nor are the responsibilities, challenges, and support needs that might distinguish Computing Ethics TAs (CETAs) from their peers in technical computing courses. This paper addresses this gap in two ways, drawing on qualitative and design research methods. First, by interviewing CETAs and their supervising faculty at two universities, we identified common motivations, struggles, and goals among CETAs. Second, drawing on this data, we developed and piloted a cross-institutional support network for CETAs on the messaging platform Discord. Despite challenges in the deployment of the server, our results indicate that cross-institutional online communities have the potential to assist CETAs in professional development, in expanding perspectives, and in strategies for dealing with difficult topics. Furthermore, our platform can be retooled for future use within and between computing ethics courses. We hope that our research will contribute to fostering community and support for CETAs as part of improving computing ethics curriculum writ large.

We apply Lave & Wenger's construct of a community of practice to identify and position members of the data work community of practice, focusing on members on the periphery who have received less attention - as compared to full practitioners (e.g., data scientists). Reporting on results of interviews with 19 civic workers who perform data work as their main task, we identify an atypical relationship between subject-domain experts (such as our interviewees) and full members of the data work community. Our interviewees may have less computational skill in data work, but they have extensive and varied practices to engage in data contextualization that data scientists and other full community members could learn from. In identifying the attributes of data workers on the periphery, we also hope to call attention to the challenges they face in performing data work in low resources institutions (e.g., governmental, non-profit). Our findings contribute to the larger conversations in human-centered data science about who performs data work and how they go about it, in order to addresses questions of power, fairness, and bias in data-intensive systems.

In this paper, we describe and analyze a workshop developed for a work training program called DataWorks. In this workshop, data workers chose a topic of their interest, sourced and processed data on that topic, and used that data to create presentations. Drawing from discourses of data literacy; epistemic agency and lived experience; and critical race theory, we analyze the workshops’ activities and outcomes. Through this analysis, three themes emerge: the tensions between epistemic agency and the context of work, encountering the ordinariness of racism through data work, and understanding the personal as communal and intersectional. Finally, critical race theory also prompts us to consider the very notions of data literacy that undergird our workshop activities. From this analysis, we offer a series of suggestions for approaching designing data literacy activities, taking into account critical race theory.

For many decades, educational communities, including computing education, have debated the value of telling students what they need to know (i.e., direct instruction) compared to guiding them to construct knowledge themselves (i.e., constructivism). Comparisons of these two instructional approaches have inconsistent results. Direct instruction can be more efficient for short-term performance but worse for retention and transfer. Constructivism can produce better retention and transfer, but this outcome is unreliable. To contribute to this debate, we propose a new theory to better explain these research results. Our theory, multiple conceptions theory, states that learners develop better conceptual knowledge when they are guided to compare multiple conceptions of a concept during instruction. To examine the validity of this theory, we used this lens to evaluate the literature for eight instructional techniques that guide learners to compare multiple conceptions, four from direct instruction (i.e., test-enhanced learning, erroneous examples, analogical reasoning, and refutation texts) and four from constructivism (i.e., productive failure, ambitious pedagogy, problem-based learning, and inquiry learning). We specifically searched for variations in the techniques that made them more or less successful, the mechanisms responsible, and how those mechanisms promote conceptual knowledge, which is critical for retention and transfer. To make the paper directly applicable to education, we propose instructional design principles based on the mechanisms that we identified. Moreover, we illustrate the theory by examining instructional techniques commonly used in computing education that compare multiple conceptions. Finally, we propose ways in which this theory can advance our instruction in computing and how computing education researchers can advance this general education theory.

In response to widespread calls for computer scientists to better engage with the ethical dimensions of their work, there has been a surge of interest to embed ethics across the computer science (CS) curriculum. Yet one key set of barriers to doing so can be broadly described as scaling challenges -- in the number and breadth of courses in a curriculum and in the number of students in the CS major. Our paper describes and makes available a novel activity for teaching ethics using role-play that has advantages for scaling across different courses and in different delivery modes, including synchronous and asynchronous online course offerings. We describe our design process and early findings from developing the activity in a large first year seminar course, a senior-level computing and society class, and three different online graduate level courses. Further, we describe an evaluation survey that instructors can use to assess the short-term impact of the activity. We analyze survey results and our direct observations to reflect on the strengths and challenges of the activity. Our experiences suggest that role-play as a pedagogical tool can be particularly useful to broaden student perspectives and meaningfully incorporate ethics into CS courses.

Data has become central to the technologies and services that human-computer interaction (HCI) designers make, and the ethical use of data in and through these technologies should be given critical attention throughout the design process. However, there is little research on ethics education in computer science that explicitly addresses data ethics. We present and analyze Re-Shape, a method to teach students about the ethical implications of data collection and use. Re-Shape, as part of an educational environment, builds upon the idea of cultivating care and allows students to collect, process, and visualize their physical movement data in ways that support critical reflection and coordinated classroom activities about data, data privacy, and human-centered systems for data science. We also use a case study of Re-Shape in an undergraduate computer science course to explore prospects and limitations of instructional designs and educational technology such as Re-Shape that leverage personal data to teach data ethics.

Gestures, or spontaneous hand movements produced when talking, are an untapped resource for understanding student knowledge in computing education. This paper develops a conceptual framework to support future studies of learning and teaching that incorporate gesture studies in programming contexts. In particular, this paper introduces how gesture has been used to study teaching and learning in another discipline, mathematics; critically reviews and interprets what concepts and methods may be most relevant to programming contexts; and also discusses what unique challenges programming contexts present to studies of gesture (e.g. differences in abstract versus concrete). We ground our understandings of gesture by using an observational study where we observed novice students learning to program. This paper concludes by suggesting potential avenues for future research in computing education that incorporate analyses of gesture in studies of teaching and learning.

An established body of work in CSCW and related communities studies social and cooperative interaction in museums and cultural heritage sites. A separate and growing body of research in these same communities is developing ways to understand the design and use of social media from a curating perspective. A curating perspective focuses on how social media is designed and used by people to develop and manage their own digital archives. This paper uses a cultural heritage museum as the empirical basis and setting along with new information visualization methods we have developed to better integrate these bodies of work and introduce the concept of personal curation; a socio-technical practice in which people collect, edit, and share information using personal information devices and social media as they move through physical environments rich with meaning potential. In doing so this paper makes three contributions. First, it illustrates how to combine a spatial focus on people's movement and interaction through the physical environment with an analysis of social media use in order to gain a deeper understanding of practices such as personal curation. Second, it shows in greater detail how visitors to museums and cultural heritage sites use and link digital information with physical information to shape others' understandings of cultural heritage. Third, it suggests how museums and cultural heritage sites may leverage personal curation to support more expansive learning opportunities for visitors.

Most of today's approaches to introductory computer science education reflect a 1970s view of computer science: students typically create single-threaded programs that do local IO. Some of the technologies used within these educational experiences include post-70s visual language elements like blocks to assist with the syntax of programming, but the semantics of the programming have generally remained frozen in time. Yet, the field of computer science has progressed enormously since the 1970s: multi-core computing, distributed systems, and machine learning are now ubiquitous, and have deep philosophical and practical ramifications for computer science education, including our definitions of what computational thinking is, and for how we design new programming languages, including visual languages. I will compare the current state of the art of novice programming tools and theories to the current state of computer science, and describe how new conceptual frameworks and visual programming tools could support contemporary computer science education.