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Short Abstract

Using crowdsourcing theory, we reframe citizen science as open, distributed problem solving. Profiling contributions (activities, knowledge, resources, decisions) and tuning design levers (tasks, allocation, rewards, information) can boost performance and inclusion - with AI as a partner.

Abstract

Crowdsourcing theories offer a powerful lens to understand and improve citizen science. Rather than treating “citizen” projects as sui generis, we frame them as a form of open, distributed problem solving where tasks, knowledge, resources, and decision rights are modularized and recombined across actors using different "crowd paradigms". Using this lens clarifies two things. First, the often-separate traditions of “Citizen Science” and “Crowd Science” study largely the same phenomenon but privilege different outcomes.

Second, a crowdsourcing perspective enables a crisp, multi-dimensional profiling of projects along four contribution types—activities, knowledge, resources, and decisions—revealing who does what, with which inputs, and who decides. This profile travels well across domains (eBird, Zooniverse, Foldit, Polymath) and makes visible where citizen contributions are essential.

Viewing citizen science as a “new form of organizing” yields a tractable agenda on four design levers: task division (granularity, timing), task allocation (self-selection vs. assignment), provision of rewards (beyond authorship to intrinsic and local value), and provision of information (training, feedback, coordination). These levers link directly to performance and inclusiveness and are amenable to rigorous testing on platforms that log processes and outcomes.

Finally, the crowdsourcing lens helps position AI not only as infrastructure but as a “third actor.” Hybrid human–machine systems now route tasks dynamically and shift volunteer effort from repetitive labeling to higher-order discovery, with implications for equity, engagement, and impact. The upshot: applying crowdsourcing theory provides a common language and actionable design principles to unlock both the productivity and democratization potential of citizen science.

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This paper develops a framework to clarify crowd involvement in science, distinguishing breadth (across project stages) and depth (type of inputs per stage). Applying it to co-created projects, we identify challenges of crowd involvement and suggest directions for research on co-creation in science.

Abstract

A growing number of research projects involve both professional scientists and members of the general public (“crowds”). Although the contributions of crowds are often limited to narrow tasks such as data collection or data processing, policy makers and science advocates call for more extensive involvement, including highly collaborative “co-creation”. However, co-creation can be defined in different ways, making it difficult to understand its potential benefits as well as challenges. To provide greater conceptual clarity, we propose a framework that distinguishes the breadth of crowd involvement across one versus multiple stages of a research project from the depth of crowd involvement at a given stage. We also argue that the key distinguishing feature of deep involvement and of co-creation is that crowd contributions go beyond effort and knowledge to include active participation in decision making. In the empirical part of the paper, we first apply this framework to characterize crowd involvement in co-created research projects from the biomedical field, covering both basic and applied research. We then analyze qualitative data to explore challenges these projects faced, focusing on aspects that related specifically to crowd participation in decision making. Our findings contribute to ongoing debates around crowd involvement in science and suggest important avenues for future research on co-production in the context of science and innovation.

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The SDG Olympiad, launched in 2024, is inspired by experimentation in managing student-driven innovation through activities like hackathons and summer schools, with citizen science as a common approach. This paper describes lessons learned and outlines a future roadmap for the SDG Olympiad.

Abstract

Citizen science can be a powerful engine for student-driven innovation when paired with a structured methodology and embedded in a global competition framework. This paper explores innovation management for citizen science through the SDG Olympiad, an international challenge that brings together interdisciplinary student teams from over a dozen universities on four continents. to create solutions based on citizen-generated data for the UN Sustainable Development Goals (SDGs).

The paper draws on examples of student-led projects developed during SDG Summer Schools, a format initiated in Geneva in 2016 and now used in several European and African universities, and SDG Open Hack events launched in Beijing in 2019 and currently attracting thousands of students in China and South-East Asia. The paper examines how citizen science methods can be systematically introduced to students to produce scalable social and technological innovations.

Central to this approach is the GEAR methodology pioneered in the European Crowd4SDG project, which involves a four-phase framework: Gather challenges, Evaluate prototypes, Accelerate projects, and Refine methodologies. In the Gather phase, international organizations and NGOs help identify sustainability challenges that can benefit from citizen-generated open data. The Evaluate phase emphasizes iterative design and testing of practical prototypes, working with local stakeholders. Acceleration involves connecting student teams with partners and investors, at both the local and global levels. Key to the evolution of the SDG Olympiad has been a constant effort to Refine the methodologies used to generate student projects.

The paper provides several concrete examples of projects that are now generating useful data for the SDGs and provides a future roadmap for how hierarchically structured innovation management, using the GEAR methodology and culminating in a global SDG Olympiad event, can spread knowledge of citizen science to universities worldwide and nurture student-driven research and entrepreneurship that directly contribute to achieving the SDGs.

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To reach a more diverse group of citizens, different research methods are identified to develop a typology of Citizen Engaged Research along two dimensions: (1) whether the goal of research is to understand or change societal problems; and (2) the phase of community engagement in research projects.

Abstract

Despite methodological developments in the field to strengthen validity and reliability of research outcomes (Bouwman & Grimmelikhuijsen, 2016; Groeneveld et al., 2015), several challenges remain a matter of concern for Public Administration scholars (Bolton & Stolcis, 2003; O'toole Jr, 2004). Most importantly, the quality of research with and among citizens in bureaucratic processes is a topic of concern among Public Administration scholars (Olsen, 2004), as citizens can act differently depending on their individual backgrounds and contexts (Dewey, 1927).

In response, some scholars have pled to increase engagement with society to develop innovative research ideas (Bushouse et al., 2011). Some scholars argue that the lack of citizens’ influence on research goals and unclarity about the purpose of research can effect citizens’ participation and inclusion (Cohen & Uphoff, 1980). In other scientific disciplines, such as public health or anthropology, engaged research is adopted to establish a mutually beneficial relationship to exchange knowledge between communities and researchers (Boyer, 1990, 1996; Holland et al., 2010).

Through a scoping review different research methods are identified to develop a typology of Citizen Engaged Research along two dimensions: (1) whether the goal of research is to understand or change societal problems; and (2) the phase of community engagement in research projects. The goal of this paper is to provide scholars with an overview of existing methods to reach a more diverse group of citizens in research, while also methodologically advancing research.

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The article presents a discussion about the role of the Poetz-Sauermann design canvas and toolbox in promoting the structured development of the CSS project. Aim is to engage in constructive dialogue with the panel of experts about responsible and effective scaling of the project using these tools.

Abstract

This presentation delineates the context and preliminary outcomes of a five-day Bachelor's workshop seminar, which was part of a career-orientation module at the University of Hamburg's Department of Social Sciences, as a component of the regular curriculum during the winter semester of 2025/2026. The seminar co-developed a Citizen Social Science project with students, commencing from the initial stages and with a focus on preparing a public dataset with the assistance of artificial intelligence.

The primary objective is to generate public value, defined as the creation of actionable insights and low-threshold artifacts for a clearly defined affected public. Ideally, this initiative will also serve to cultivate a citizen-scientist community that contributes indigenous and local knowledge. The community shall refine, improve, or contest the public data, and shall also generate new research and analysis questions.

Seminar activities include problem scoping, stakeholder mapping, contribution paths, consent and data-handling plans, and piloting simple workflows. Students participate in the design and implementation of all of these activities. Ethical and evaluation principles are integrated into the project's design at all stages.

The presentation explores the potential of the design canvas and toolbox to assist in the structuring and organization of the project. Additionally, it investigates the means by which these tools could facilitate the responsible scaling of participation and impact within the context of this Citizen Social Science project.

Ideally , a pragmatic discourse would be initiated with organizational and strategy experts, resulting in a collaborative formulation of subsequent actions for project expansion.

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Short Abstract

We reframe citizen science through five paradigms and discuss how AI can automate, augment, or manage citizens in each. We illustrate with a range of examples across scientific fields. We also discuss how and when AI may challenge the underlying logic of involving large crowds in the first place.

Abstract

Citizen science is often treated as a single model—volunteers contribute data to professional science—yet projects differ markedly in how tasks are structured, how coordination occurs, and what kinds of knowledge are produced. We propose a comparative framework that locates citizen science within five “crowd paradigms” (Beck et al. 2022). For each paradigm, we analyze how contemporary AI systems interact with core mechanisms of volunteer contribution. Rather than offering design prescriptions, we develop an explanatory account of when and why AI may augment human effort (e.g., pre-screening images, flagging anomalies), automate citizens' tasks, or manage projects (e.g., routing tasks, estimating reliability, shaping incentives). Going beyond the automation of individual citizen tasks, we also discuss how AI may challenge the overall logic of involving crowds in the first place.

The framework clarifies cross-cutting issues—independence and diversity of judgments, error structures and bias propagation, validity and provenance, and the social meanings of participation—that are often discussed piecemeal. It helps interpret observed successes and failures across domains such as ecology, astronomy, health, and mapping by linking outcomes to underlying mechanisms. We highlight boundary conditions where AI is most complementary to volunteers (e.g., when human local knowledge or tacit skills matter) and where substitution risks are highest (e.g., routine perception tasks at scale). We also surface implications for inclusion, motivation, and ethics.

Our contribution is a parsimonious map that integrates heterogeneous citizen-science practices and situates AI within them. This map can organize empirical findings, suggest comparable measures across projects, and guide cumulative research on the joint roles of human crowds and AI in knowledge production—without presuming any single “best” design.

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We present an AI-supported platform that curates teachers’ practice-based narratives into a knowledge network, reconfiguring scientist–teacher dynamics and fostering scalable, inclusive learning across citizen science projects.

Abstract

School Participation in Citizen Science (SPICES) often unfolds within entrenched power relations, where scientists’ authority dominates and teachers are positioned at the margins. This risks silencing tacit pedagogical knowledge, even though such knowledge is essential for SPICES.

We address this challenge in the context of a network of citizen science partnerships, established within the Taking Citizen Science to School (TCSS) center, which brings together multiple SPICES projects. The network’s aim is to foster shared learning about integrating citizen science into schools. To support this goal, we developed a Web-based platform that curates a research-based repository of pedagogical design principles. Teachers’ narratives of practice, co-authored with dedicated chatbots are brought into workshops, where heterogeneous groups, including the storyteller, reflect on these stories and link them with relevant pedagogical principles. Through this work, participants generate a growing knowledge network that connects stories and principles across projects, contexts and expertise.

Our empirical analysis of workshop discussions shows how teachers’ voices, once peripheral, became central to shaping collective understanding. For example, when a teacher’s story from a digital humanities SPICES project was linked to a pedagogical design principle, a scientist from an environmental psychology project immediately recognized its relevance, sparking further rounds of cross-project learning. This process generated knowledge about integrating citizen science into school, forged only through the interplay of perspectives. In doing so, the workshops and the underlying platform reconfigure center–periphery dynamics by bringing teachers’ contributions to the heart of knowledge creation within citizen science partnerships.

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Short Abstract

This presentation shares the findings of two studies: first examining dispositional regulatory focus among participants, then testing, via a nudging experiment, how theory-based communication can impact engagement and retention in Mosquito Alert, a globally active digital citizen science project.

Abstract

The proposed presentation will bring together findings from two interlinked studies that apply psychological theory to advance the management and design of citizen science initiatives within the domains of ecological monitoring and public health.

The first study examines the prevalence of dispositional regulatory focus among volunteers in Mosquito Alert, a large-scale, app-based platform for monitoring invasive vector mosquitoes. The findings demonstrate a skewed distribution, as most participants exhibit a prevention-oriented goal pursuit style that would emphasize aspects such as safety, vigilance, and risk avoidance. Rarely captured through conventional motivation surveys, the study provides valuable insight into psychological drivers of participation and persistence.

Building on these insights, the second study applies behavioral economics and nudging techniques to test how communication strategies can be tailored to participants’ goal orientations. By experimentally framing goal-congruent messages in either promotion (eager, aspirational) or prevention (vigilant, protective) terms, the study investigates how these subtle differences in communication influence willingness to engage, intensity of participation, and likelihood of retention, by analyzing trends in direct measurement of behavior rather than self-reported intent.

Collectively, the findings of the two studies aim to offer pathways from theoretical insight to organizational practice. They highlight the importance of moving beyond broad motivational categories toward deeper psychological mechanisms that can inform the design, communication, and scalability of citizen science, with the aim of contributing to building a more inclusive, effective, and resilient citizen science ecosystems.

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The qualitative study reveals the background, management, and consequences of responsive and response-able project coordination within Citizen Social Science, placing particular emphasis on the situated practices of knowledge production, caring strategies, and their implications for future projects.

Abstract

Good care and services of general interest, equal health opportunities in urban districts, social and cultural diversity in urban society, or collaborative research on social cohesion — Citizen Social Science projects are gaining increasing popularity. In contrast to Citizen Science projects situated in the natural sciences and often focused primarily on data collection, e.g. crowdsourcing, Citizen Social Science projects engage citizen scientists more deeply, challenging and decentering hierarchical and hegemonic ways of doing social science.

This article examines how different forms of knowledge and perspectives of diverse target groups and practitioners can be integrated into Citizen Social Science. It identifies challenges, conditions for success, and conceptual as well as methodological particularities from the perspectives of project coordinators. To this end, six practical cases were analyzed. Data was collected using guided expert interviews and a structured group discussion with Citizen Social Science project coordinators in Germany.

Drawing on Grounded Theory (Strauss and Corbin, 1990), the qualitative study reveals that Citizen Social Science coordinators perform Citizen Science as a responsive project situated between multiple horizons of expectation. Consequently, response-able practices, in the sense of Haraway (2016), shape every stage and dimension of Citizen Social Science projects — leading to ongoing project adaptation and the relational emergence of situated knowledge(s).

With this paper, we aim to illuminate the background, management, and consequences of such responsive and response-able project coordination, placing particular emphasis on the situated practices of knowledge production, caring strategies, and their implications for future Citizen Science projects.

References

Haraway, D. J. (2016). Staying with the Trouble: Making Kin in the Chthulucene. Duke University Press.

Strauss, A., & Corbin, J. M. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Sage Publications, Inc.