Background: The motivational interviewing (MI) approach has been shown to help move ambivalent smokers toward the decision to quit smoking. There have been several attempts to broaden access to MI through text-based chatbots. These typically use scripted responses to client statements, but such nonspecific responses have been shown to reduce effectiveness. Recent advances in natural language processing provide a new way to create responses that are specific to a client’s statements, using a generative language model. Objective: This study aimed to design, evolve, and measure the effectiveness of a chatbot system that can guide ambivalent people who smoke toward the decision to quit smoking with MI-style generative reflections. Methods: Over time, 4 different MI chatbot versions were evolved, and each version was tested with a separate group of ambivalent smokers. A total of 349 smokers were recruited through a web-based recruitment platform. The first chatbot version only asked questions without reflections on the answers. The second version asked the questions and provided reflections with an initial version of the reflection generator. The third version used an improved reflection generator, and the fourth version added extended interaction on some of the questions. Participants’ readiness to quit was measured before the conversation and 1 week later using an 11-point scale that measured 3 attributes related to smoking cessation: readiness, confidence, and importance. The number of quit attempts made in the week before the conversation and the week after was surveyed; in addition, participants rated the perceived empathy of the chatbot. The main body of the conversation consists of 5 scripted questions, responses from participants, and (for 3 of the 4 versions) generated reflections. A pretrained transformer-based neural network was fine-tuned on examples of high-quality reflections to generate MI reflections. Results: The increase in average confidence using the nongenerative version was 1.0 (SD 2.0; P=.001), whereas for the 3 generative versions, the increases ranged from 1.2 to 1.3 (SD 2.0-2.3; P<.001). The extended conversation with improved generative reflections was the only version associated with a significant increase in average importance (0.7, SD 2.0; P<.001) and readiness (0.4, SD 1.7; P=.01). The enhanced reflection and extended conversations exhibited significantly better perceived empathy than the nongenerative conversation (P=.02 and P=.004, respectively). The number of quit attempts did not significantly change between the week before the conversation and the week after across all 4 conversations. Conclusions: The results suggest that generative reflections increase the impact of a conversation on readiness to quit smoking 1 week later, although a significant portion of the impact seen so far can be achieved by only asking questions without the reflections. These results support further evolution of the chatbot conversation and can serve as a basis for comparison against more advanced versions.

Agriculture is foundational for food security on our planet. Considering climate change and other pressures on food production, HCI scholars have increasingly begun to examine how the field should approach agricultural innovation. We conducted a literature review of HCI research through the lens of competing future visions for good food systems : a “conventional” vision of profit-oriented production, and an “alternative” which prioritizes sustainability and community-led practices. Leveraging the concept of sociotechnical imaginaries, we provide an empirical analysis of how HCI and adjacent applied computing projects align with these competing visions for agriculture. This review reveals, amongst other findings, a prioritization of the perspectives of the Global North and a need for more careful attention to the constraints and aspirations of subsistence farmers. Finally, we note the limits of the conventional-alternative binary that shapes much of contemporary HCI research focused on agriculture and offer opportunities for transcending this framing.

This mixed method study situated in Ontario, Canada, investigates how migrant farmworkers’ experiences with agricultural technologies (agtech) affect their attitudes, conditions, and expectations of work, and how workers envision technologies serving as supportive interventions. Through a survey and interviews, we identify that surveillance and tracking agtech (chequeadoras) affect workers, imparting negative health and safety consequences. Workers’ interactions with chequeadoras reveal three major impacts: performance expectations engender stress, surveillance causes fears of disciplinary action, and performance tracking heightens competition. These impacts demonstrate how chequeadoras erode workers’ capacity to build sociality and solidarity. In response to these impacts and to support workers’ desired workplace changes, which aim for safer environments with technical skill development opportunities, we examine tactics from HCI, critical design, and migrant justice movements. Our findings lead us to contemplate what qualifies as agtech and how we may support marginalised workers with divergent opinions regarding workplace technologies, and desired collective change.

While climate change has been a longstanding concern of HCI and CSCW communities, this scholarship has rarely drawn attention to the well-documented pattern of minoritized and marginalized communities unfairly carrying the brunt of environmental burdens. Through this one-day remote workshop, we plan to critically extend how CSCW can support climate action by focusing on two social movements, environmental and climate justice, both of which aim to reduce environmental degradation and pursue sustainable communities without doing so at the expense of others. In this workshop, we aim to identify how CSCW and datafication have helped to uphold environmental or climate justice commitments or has been complicit in producing or maintaining environmental harms. We also plan to discuss and identify a CSCW research agenda addressing how to support climate justice principles and processes in designing technologies and systems. We hope that this workshop will help to initiate and foster a longer-term relationship with researchers, activists and practitioners who are engaging with or interested in climate justice in computing.

Digital upskilling and remote work are frequently presented as pathways for displaced communities who face political, economic crises and employment barriers. The viability and stability of available energy infrastructures for these communities is critical to the long-term success of these proposed job trajectories. In this paper, we investigate how 17 Syrian refugees living in Lebanon navigate their local unstable energy infrastructures to conduct digital gig work and receive digital training. We found that digital gig work and training are workarounds to the political and social inaccessibility of local labor markets for refugees and that participants rely on a series of material energy strategies to optimize their electricity access. We argue that to support refugees conducting digital gig work and training, we must recognize and account for ecological, social, and technological limitations and frailties in the development of technological solutions and draw our attention to how infrastructures are perpetually undergoing processes of breakdown, repair, and renewal. We argue that this attention to ongoing transformation and renewal creates new opportunities for productive and creative reconfiguration as well as modes through which CSCW may intervene in unstable energy contexts. From this perspective, we emphasize the importance of better resourcing displaced communities with information regarding energy access and supporting them in establishing and strengthening their own visions for alternative energy systems and employment pathways.