A growing concern for the field of computer-human interaction is human interaction with artificial intelligence (AI). A concern about the use of AI tools is that automating tasks reduces the opportunity to learn how to do them. We explore antecedents to this outcome in the context of AI tools for programming in an introductory class. We hypothesize that if students are intrinsically motivated to learn to program, they may avoid using tools in order to engage with the material, while students with extrinsic motivations may use the tools to get work done. Counter to our expectations, analysis of a survey of learning motivations and log data of AI tool use suggests that students with higher extrinsic motivation actually use the AI tool less, while those with higher intrinsic motivation use it more, and that there is no correlation between the level of AI use and grades. These findings suggest that to understand the impact on skills, it is necessary to examine how AI is used in detail, not just the overall level of use.

This report is an outcome of a Computing Community Consortium (CCC) visioning workshop on Grand Challenges for the Convergence of Computational and Citizen Science Research conducted on April 8-9, 2025, in Washington, D.C. as well as through several precursor virtual input-gathering sessions. These events brought together experts across relevant disciplines to develop a research agenda that brings to fruition the above vision on how humans and machines may team up to solve some of the world's most pressing scientific problems. Citizen science delivers measurable economic and national value. Public participation in scientific research generates millions of dollars in volunteer labor value, extends government agency capacity, and directly supports federal priorities in areas such as disaster management, public health, water, energy, workforce development, and many more. At the same time, 21st-century scientific infrastructure requirements for citizen science (from hardware and cyberinfrastructure to data and computational frameworks) mirror those for computational science more generally. The distributed, collaborative, long-term, and contextual nature of citizen science makes it a demanding real-world use case for a novel robust research infrastructure that accounts for security, privacy, resource adaptability, and transparency. In this report, we outline the key findings, future research directions, and recommendations that emerged from the April 2025 CCC Grand Challenges for the Convergence of Computational and Citizen Science Research Workshop.

As AI becomes more capable, it is unclear how human creativity will remain essential in jobs that incorporate AI. We conducted a 14-week study of a student newsroom using an AI tool to convert web articles into social media videos. Most creators treated the tool as a creative springboard, not as a completion mechanism. They edited the AI outputs. The tool enabled the team to publish successful content that received over 500,000 views. Human creativity remained essential: after AI produced templated outputs, creators took ownership of the task, injecting their own creativity, especially when AI failed to create appropriate content. AI was initially seen as an authority, due to creators' lack of experience, but they ultimately learned to assert their own authority.

Members of cooperative groups can work together more effectively if they develop a shared classification schema, but distributed groups face barriers to doing so. To better understand how classifications and classification practices can emerge and support the work of distributed groups, we review the literature on folksonomies (a kind of shared classification schema) in crowdsourcing projects (one type of distributed work). The review yields three potentially productive tensions associated with the development of folksonomies in crowdsourcing projects. First, projects must establish who has the authority to decide on adopted terminology and with what consequences. Second, there can be tension if people who tag objects have different interests in tagging than those who use the tags to search for content. Finally, projects must decide when to intervene to maintain a balance between a stable vocabulary and the ability of the project to accommodate ongoing changes. We illustrate these tensions by comparing how they are handled in the photo-sharing site Flickr, the story-sharing site Archive of Our Own (AO3), the internet culture classification site Know Your Meme, and the citizen science project Gravity Spy. The comparison suggests guidelines for project managers regarding the identified tensions.

Many organizations actively plan to introduce artificial-intelligence-based (AI) applications to solve business problems, improve efficiency, and promote innovation. However, in this process, organizations face challenges in identifying AI application use cases as little research has focused on approaches for identifying them. Following the design science research paradigm, this paper designs an idea generation technique to enable employees to come up with AI use case ideas. The idea generation technique proposed is based on shadow IT usage analysis. Shadow IT represents all hardware, software, or any other IT solutions used by employees or business departments that have not received formal IT department approval. Shadow IT reflects perceived user needs that the official IT does not support. Further, using shadow IT to solve problems may also requires human intelligence, thus indicating potential applications for AI. This paper applied and evaluated the technique in a Chinese enterprise. This study demonstrates that considering shadow IT usage can give employees insights to develop AI use cases ideas by identifying deficiencies in information systems and places where they deploy their own intelligence. This research addresses a relevant practical problem and contributes to helping organizations to identify AI application business scenarios, specifically to identify employees’ AI use cases ideas about enhancing existing information systems.

The growing agency of artificial intelligence (AI) systems, more specifically systems based on machine learning, has raised concerns about the security, safety, and ethical risks of AI use. We argue that core to mitigating AI risks is proper alignment of control and accountability for the stakeholders involved in AI development and use. Control enables, and accountability motivates, stakeholders to achieve desired and avoid undesired outcomes using AI. However, AI systems' capabilities for autonomous adaptivity reduce control even for the experts who create them. Moreover, increasing interdependencies between AI development and use render it difficult to unambiguously locate control and accountability. In this paper, we address these challenges for mitigating AI risks by postulating decentralized forms of stakeholder governance and integrative negotiations among stakeholders during the AI life cycle as conducive to aligning control and accountability for AI development and use. Further, we specify that extensive information sharing aided by perspective taking and a shared norm of accountability facilitate integrative negotiation strategies. We conclude by discussing the implications of our theory for management scholarship on the impact of AI, and identify promising avenues for future research at micro, meso, and macro levels of analysis.

Introduction. Deskilling is a long-standing prediction of the use of information technology, raised anew by the increased capabilities of AI (AI) systems. A review of studies of AI applications suggests that deskilling (or levelling of ability) is a common outcome, but systems can also require new skills, i.e., upskilling. Method. To identify which settings are more likely to yield deskilling vs. upskilling, we propose a model of a human interacting with an AI system for a task. The model highlights the possibility for a worker to develop and exhibit (or not) skills in prompting for, and evaluation and editing of system output, thus yielding upskilling or deskilling. Findings. We illustrate these model-predicted effects on work with examples of current studies of AI-based systems. Conclusions. We discuss organizational implications of systems that deskill or upskill workers and suggest future research directions.

We explore patterns of interaction with different learning resources (e.g., forums) to predict learning outcomes in an online citizen science project called Gravity Spy. To explore how volunteers engage with and benefit from these resources, we categorize them based on Sørensen's three forms of presence in learning environments: authority-subject, agent-centered, and communal presence. Methodologically, we apply sequence analysis to traces of volunteer interactions with the project to identify engagement patterns with these resources that predict learning. Our interpretation of these patterns is augmented by insights gleaned from interviews with volunteers about their work and use of learning resources. We find that early in the project, volunteers have only a simple task to learn, and completing that task is most predictive of their learning. At more advanced levels, when tasks become more complex, discussions with other volunteers become increasingly important, and interaction patterns become more varied. Viewing learning as a series of routines allows us to articulate precisely how and in what context learning occurs. We conclude by discussing the implications of these findings for designing citizen science projects that promote learning.

With the development of AI technologies, especially generative AI (GAI) like ChatGPT, GAI is increasingly assisting people in various tasks. However, people may have different requirements for GAI when using it for different kinds of tasks. For instance, when brainstorming new ideas, people may want GAI to propose different ideas that supplement theirs with different problem-solving perspectives, but for decision-making tasks, they may prefer GAI adopt a similar problem-solving process with people to make a similar or even the same decision as they would. We conducted an online experiment examining how perceived similarities between GAI and human task-solving influence people's intention to use GAI, mediated by trust, for four task types (creativity, planning, intellective, and decision-making tasks). We demonstrate that the effect of similarity on trust (and so intent to use AI) depends on the type of task. This paper contributes to understanding the impact of task types on the relationship between perceived similarity and GAI adoption, with implications for future use of GAI in various task contexts.

The first successful detection of gravitational waves by ground-based observatories, such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), marked a breakthrough in our comprehension of the Universe. However, due to the unprecedented sensitivity required to make such observations, gravitational-wave detectors also capture disruptive noise sources called glitches, which can potentially be confused for or mask gravitational-wave signals. To address this problem, a community-science project, Gravity Spy, incorporates human insight and machine learning to classify glitches in LIGO data. The machine-learning classifier, integrated into the project since 2017, has evolved over time to accommodate increasing numbers of glitch classes. Despite its success, limitations have arisen in the ongoing LIGO fourth observing run (O4) due to the architecture's simplicity, which led to poor generalization and inability to handle multi-time window inputs effectively. We propose an advanced classifier for O4 glitches. Using data from previous observing runs, we evaluate different fusion strategies for multi-time window inputs, using label smoothing to counter noisy labels, and enhancing interpretability through attention module-generated weights. Our new O4 classifier shows improved performance, and will enhance glitch classification, aiding in the ongoing exploration of gravitational-wave phenomena.

The recent addition of data journalists to several dozen U.S. public radio newsrooms has created multiple new hybridities in the form. No longer are numbers and large datasets “audio poison.” Instead, they are an essential tool for these journalists, who prize journalism’s interpretive function, expressing information in new ways and challenging conventions of broadcast newsroom employment. This study, which relies on semi-structured interviews with 13 public radio data journalists, uses Carlson’s boundary work typology to analyze the ways in which data journalists are expanding the boundaries of U.S. public radio journalism, as well as ways in which they have pushed back against expulsionary pressures. This study’s findings problematize the idea that the results of boundary work must be expressed as in-or-out proposition. Rather, U.S. public radio data journalists suggest their boundaries are a continuum where they may be conditionally accepted by their colleagues, depending on deadlines and on the skills possessed by non-data journalists.

We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the zone of proximal development (ZPD) concept, which allows us to describe AI augmentation of human learning, human augmentation of machine learning, and how tasks can be designed to facilitate co-learning. The study takes a design-science approach to explore the design, deployment, and evaluations of the Gravity Spy citizen science project. The findings highlight the challenges and opportunities of co-learning, where both humans and machines contribute to each other’s learning and capabilities. The study takes its point of departure in the literature on co-learning and develops a framework for designing projects where humans and machines mutually enhance each other’s learning. The research contributes to the existing literature by developing a dynamic approach to human-AI augmentation, by emphasizing that the ZPD supports ongoing learning for volunteers and keeps machine learning aligned with evolving data. The approach offers potential benefits for project scalability, participant engagement, and automation considerations while acknowledging the importance of tutorials, community access, and expert involvement in supporting learning.

PurposeResearch on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.Design/methodology/approachWe investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.FindingsDifferent interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.Originality/valueFindings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

We identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI’s inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.