The second-ever CCC Community Chat, A New Era of Scientific Progress: Uniting Computational and Citizen Science for Advanced Research, took place on Wednesday, May 20, 2026. Moderated by CCC Council Member Michela Taufer (University of Tennessee, Knoxville), this virtual webinar featured the co-authors of the recent Grand Challenges for the Convergence of Computational and Citizen Science Research workshop report as they presented key findings and took questions from the audience about how advanced computing and public participation in science can mutually enrich each other.
Both the Community Chat and the CCC workshop report — co-authored by Lucy Fortson (University of Minnesota), Lea Shanley (International Computer Science Institute, Berkeley), Tanya Berger-Wolf (The Ohio State University), Kevin Crowston (Syracuse University), Corey Jackson (University of Wisconsin-Madison), and Saiph Savage (Northeastern University) — center around two fundamental questions: why is the convergence of citizen science and advanced computing needed, and how do we make it happen on a large scale?
The Need for Convergence
There are a number of gaps in the current scientific research ecosystem that converging computational and citizen science research can help fill. Corey Jackson presented the findings of the report which aims to answer the two above questions.
1. Mutual reinforcement between AI/ML and citizen science
There is an underutilized feedback loop between AI/ML and citizen science, where each can train and improve the other.
In short, as Jackson explained, “Citizen science gives contextualized training data, and AI gives citizen science real-time coaching and the ability to scale up.”
2. Human-computer teaming is essential
Bringing citizen science and computational science together makes the most of the unique strengths of humans and computers while helping mitigate their weaknesses.
“Even the best models drift. They generalize poorly across instruments and oftentimes miss context… Humans can catch what the AI misses and the AI can handle what humans can’t do at scale.”
3. Feedback and interactivity are critical for engagement
“Feedback is a gap that is largely treated as an afterthought,” Jackson said. Despite this, personalized, real-time, and reciprocal feedback drives participant retention and helps ensure data quality.
4. Trust is fragile but foundational
Jackson made sure to highlight the importance of user trust in citizen science research projects, and how all other factors need to be addressed to help maintain that trust.
“Opaque algorithms, inaccessible interfaces, they all kind of erode the willingness of volunteers to contribute and the willingness of scientists to use the data.”
5. Infrastructure as bottleneck and opportunity
Out-of-date infrastructure hinders the success of citizen science, but revitalizing it promises a new capacity to organize increasingly global research projects that handle larger amounts of and more complex data.
“Most citizen science platforms launched around 2006, the same era as the smartphone…” Jackson noted. “Today they’re running globally on devices ranging from drones to low-cost sensors, and in places with intermittent connectivity. The infrastructure hasn’t caught up.”
6. Rising security, privacy, and adversarial threats
Data poisoning, synthetic media, and coordinated disinformation all pose threats to citizen science research.
“As citizen science feeds into agency decision-making — NASA, NOAA, for example — it becomes a target… The defenses we [have] are underdeveloped.”
7. Momentum is real but fragmented
Finally, there can be a certain instability around citizen science research projects that hinders their potential. Currently, there is a lack of shared standards, sustained funding, and coordinated governance that would help these projects succeed long-term.
“We have a ton of wins. There are a ton of really great projects. But we really need to be thinking about how we share knowledge to make these projects and these approaches more sustainable,” Jackson urged.
A Research Roadmap for the Next Decade
To address those gaps, the co-organizers synthesized findings from the workshop into five key research drivers:
- Human-Machine Teaming: How humans and machines should divide labor and research workflow.
- Feedback, Interactivity, and JIT Delivery: The full feedback loop between volunteers, project teams, and society.
- Trust, AI, and Citizen Science: Building a shared future on transparency, governance, and measurable trust.
- Security, Privacy, and Open Systems: Keeping participatory systems both open and secure.
- Future Infrastructure: Foundational, sustainable infrastructure across cyber, data, human, and physical layers.
These research drivers shaped the recommendations presented in the report for the future of convergence.
“[The question was] not what we can do in the next year, but what the field can look like in ten years if we invest deliberately now.” — Corey Jackson
Citizen Science Across Domains
The discussion was also enriched by the addition of two unique lightning talks showcasing the value of citizen science in different scientific domains — and how it intersects with advanced computing to create that value.
The first lightning talk was presented by report co-author Tanya Berger-Wolf, who presented about how ecological citizen science research has already helped push the boundaries of computing research. “The bulk of nature data today comes from citizen science contributions,” Berger-Wolf explained, and those contributions provide large, ML-ready datasets that provide the foundations for some computing research. “These datasets are the biggest source of AI and ML benchmarks, especially image-based ones.”
Berger-Wolf was followed by Marc Kuchner, Citizen Science Officer at NASA. Kuchner highlighted another research dynamic already taking place at NASA: citizen scientists writing and using their own AI tools. One NASA volunteer, for example, developed a new AI tool, uniquely suited to the research project he was involved in, to help identify clouds produced by pollution from rockets. At the same time, he pointed out that researcher trust could be just as fragile as volunteer trust in citizen science projects: “With AI/ML, anyone can be a scientist, or pretend to be one. That makes educating and guiding the public via real citizen science even more important, rewarding, and challenging.”
Watch the Webinar and Read (and Cite!) the Report
If you missed the event or want to revisit the discussion, you can watch the full recording above and read the report here:
Grand Challenges for the Convergence of Citizen Science and Computational Research
We encourage you to share the report and webinar recording widely with your colleagues, collaborators, and especially with students and early-career researchers who are shaping the future of citizen science and computing research.
Tune in to the CCC LinkedIn Showcase Page for updates and more Community Chats like this. Stay connected with CCC for the latest insights, publications, and opportunities to engage by subscribing here.
