- Convenors:
-
Benjamin Sawicki
(ETH Zurich)
Olivia Höhener (University of Zurich)
Send message to Convenors
- Format:
- Workshop
- Location:
- Stage
- Sessions:
- Tuesday 3 March, -
Time zone: Europe/Helsinki
Short Abstract
How can we use AI to facilitate inclusive participation in citizen science — not by collecting data from citizens, but by enabling their voices and knowledge? This hands-on workshop explores and prototypes AI tools for bridging social, geographic, and institutional divides.
Description
AI is typically seen as a centralising technology — developed in global tech hubs, trained on massive datasets, and applied without context. This workshop reverses that logic: we explore how AI can be reimagined as a facilitator of dialogue, civic expression, and context-aware participation in citizen science, especially in communities often considered peripheral — geographically, institutionally, or culturally.
We invite researchers, practitioners, designers, and technologists to share case studies, prototypes, and critical perspectives on AI-enhanced participation. We especially encourage contributions that critically explore AI as a participatory tool and use AI to enable dialogue, facilitate reflective engagement, or support inclusive participation - rather than to extract citizen input to train models.
The workshop aims to achieve two things:
1) To open a transdisciplinary conversation: How can AI be embedded ethically and meaningfully in citizen science? What roles can it play beyond efficiency? And how do citizens perceive and interact with such systems?
2) To co-create new forms of AI-mediated engagement that amplify local voices rather than extract data
In the two-part session, we will:
1) Explore critical examples where AI supports decentralised and inclusive citizen engagement. Map out real-world participation challenges across centre-periphery divides
2) Reflect together on ethical, technical, and participatory implications
Expected output: A shared repository of use cases, design patterns, and principles for deploying AI as a civic interface in citizen science. This will be documented and shared publicly after the event.
Accepted contributions
Session 1 Tuesday 3 March, 2026, -Short Abstract
We present Schweizer Solargeschichten, an AI-powered chatbot that captures and contextualizes citizen perspectives on solar energy. Instead of extracting data, the system fosters dialogue, reduces misinformation, and enables inclusive participation in Switzerland’s energy transition.
Abstract
Schweizer Solargeschichten emerged from a practical dilemma in citizen science: residents were willing to discuss their experiences with solar energy, but refused to share financial data through traditional surveys or forms. Personal interviews proved too resource-intensive to scale. In response, we co-developed an AI chatbot with citizens to facilitate these conversations in an accessible, conversational format.
The chatbot invites users to share motivations, doubts, and lived experiences related to solar installations. It also responds—sometimes normatively—by clarifying misconceptions or introducing factual context. Technically, it relies on retrieval-augmented generation (RAG) to ground its answers in expert knowledge while avoiding hallucinations. Yet this design choice introduces a new layer of mediation: the AI does not only elicit information but also subtly frames what counts as “relevant” or “correct” knowledge.
Through over fifty dialogues, we found that participants appreciated the opportunity to express themselves anonymously and conversationally. However, their responses also reveal ambivalence toward AI as a conversational partner—both trusted and contested. The chatbot thus exposes an inherent tension between inclusion and control: enabling open participation while encoding expert perspectives in its very structure.
Our contribution reflects on this tension and the ethics of “normative design” in participatory AI. We argue that such systems can democratize engagement only when their epistemic framing is made visible and negotiable—turning AI not into an authority, but into a transparent, fallible interlocutor in collective learning about the energy transition.
Short Abstract
We share reflections on an AI-enhanced redesign plan of a mental health citizen science chatbot, leveraging its rich dataset to build a digital participatory tool that serves as an individualized resource, offering inclusive support, fostering adaptive dialogue, and amplifying community voices.
Abstract
CoAct for Mental Health is a citizen social science initiative that individuals with mental health lived experiences and their families as co-researchers. They documented their personal experiences on mental health social support networks through micro-stories. A Telegram chatbot then disseminated these micro-stories, inviting subscribers to reflect on whether they, and someone around them, have ever lived similar experiences. More than 11,000 responses from more than 400 participants were recorded, generating a rich dataset that also includes sociodemographic profiles and indicators of emotional well-being.
We propose to study this dataset through the lens of social support networks, using AI and network-based modelling from a complex systems perspective. This analysis uncovers patterns of participation and identifies key lived experiences linked to specific sociodemographic profiles, revealing how mental health experiences emerge and can be strengthened within communities. This approach gives value to the voices shared, transforming them into actionable knowledge to enhance mental health services.
Building on these insights, it is possible to reflect on how AI can help the chatbot to adapt dynamically to participants. In this way, we want to sustain engagement more effectively while fostering self-reflection and deliver more informative data to keep studying social support networks in a participatory and open manner.
Ultimately, the envisaged AI-driven redesign would like to offer more inclusive support, empowering citizens, and providing a data-informed foundation for improving mental health care and policy. Therefore, this participatory platform bridges the gap between social needs expressed by communities and institutional policymaking.
Short Abstract
We used AI to visualize young people’s ideas for improving their city. They photographed places in Konstanz and shared their visions for change. AI-generated before-and-after images made these ideas tangible and sparked discussions about possibilities and challenges.
Abstract
In our project Konstanz Capture & Connect, four sociology students from the
University of Konstanz explored how artificial intelligence (AI) can be used not only
as a data-processing tool but as a means to visualize ideas and stimulate
participation. Working with young people from a local youth center, we invited them to
photograph places in Konstanz that they would like to change or improve. Together,
we discussed their visions for social and ecological sustainability, which we then
transformed into AI-generated images.
The resulting before-and-after visuals turned abstract ideas into tangible, discussable
realities. Through these images, participants could see their own thoughts reflected
— sometimes surprisingly, sometimes imperfectly — but always as a starting point
for dialogue. This process showed how AI can foster citizen engagement by providing
a visual language for imagination and community-based reflection.
In the workshop, we would like to share our experiences of using AI as a participatory
tool and discuss both the creative potential and the ethical and social challenges it
entails, particularly in working with young audiences. We are especially interested in
how visual methods can bridge the gap between imagination and collective action in
citizen science.
Short Abstract
We developed an AI-powered camera-enabled bird feeder capable of individually identifying wild birds. Through our citizen science programme these feeders are distributed across Europe. Data collected at these feeders is used to engage citizens with wildlife and science through a mobile app.
Abstract
We have developed a bird feeder that is able to take pictures and videos of birds and uses AI to individually identify visiting birds and obtain other information from them that is relevant for ornithological research. Through our citizen science programme, we distribute these bird feeders to citizens across Europe making each garden a study population.
Beyond counting on the citizens’ help to collect data on the local bird wildlife for ornithological research, we have designed a unique experience that connects people with nature and science, empowering them to contribute meaningfully to wildlife research beyond simple data collection.
Our bird feeder comes with an interactive mobile app that allows users to see their visiting birds and name them, developing a personal connection with each individual in the flock. Our app also includes gamification components that allows users to learn more about their visiting avian friends and science. Bird enthusiasts are known to possess above-average knowledge of the scientific process and strong ecological literacy. Building on this foundation, our project (through our app) encourages citizens to contribute directly to the research agenda by allowing them to propose or vote on topics and questions in the field of ecology, evolution and conservation that they would like to see being addressed using the collected data. This approach offers citizens a rare opportunity to engage directly with science beyond contributing data or resources, while providing scientists with feedback from hundreds of people and deeper insight into the drivers of public curiosity and ecological concern.
Short Abstract
The involvement of forest visitors in combination with AI technologies offers previously unexplored potential for forest structure identification. Citizen Scientists actively participate in forest monitoring by taking photos, which are then evaluated by AI to assess the condition of forests.
Abstract
Forests are increasingly exposed to diverse stress factors associated with climate change. Although existing monitoring programs already provide valuable information, their spatial and/or temporal significance is limited. MeineWaldKI (lit. MyForestAI) addresses this gap in research, by combining citizen science efforts and AI in a novel approach to monitor the ecological forest conditions.
MeineWaldKI is an app-based project, that explores the suitability of AI for assessing the ecological condition of forests based on photographs. In order to create a large dataset, the project team is relying on forest pictures taken by the public. A key part of the project is therefore to study how effectively citizen science can complement traditional forest monitoring and what is needed to make projects like this appealing to potential citizen scientists in across forest types. Throughout their participation, citizen scientists will be given the opportunity to gain knowledge of forest conservation and management, specifically tailored to different target groups. They are given the chance to try out gamification elements such as challenges and quizzes to test their forest knowledge, and to participate in in-person events such as guided tours and workshops. They will also be given real time AI-assisted feedback on their forest photographs, and what the forest structures recognized in the photographs may mean for forest health.
The MeineWaldKI project aims to serve as both a data collection tool and an educational platform, transforming casual forest visitors into active participants in ecological forest monitoring while also fostering environmental awareness.
Short Abstract
With the rise of AI across the participatory sciences, it is important to understand what it means to use AI responsibly, ethically, and in a trustworthy manner, so that we can ensure future use is beneficial to all in the community.
Abstract
I ran a small questionnaire study on what how those who work in citizen science consider the terms "responsible, ethical, and trustworthy" when it comes to the use of AI. What are the opportunities and drawbacks of using AI techniques? What does it mean to be responsible, ethical, and trustworthy? How does the use of AI affect volunteers, and how can we ensure they remain engaged and understand their vital role in research? And perhaps most importantly, how does the participatory science community think the use of AI should be advanced?