 CCC Blog Citizen science projects have contributed to scientific progress across disciplines. From users mapping biodiversity on iNaturalist, to analyzing protein folding configurations to advance drug discovery on Foldit, to discovering new planets on Zooniverse, we have seen the value of engaging everyday participants in scientific research projects.
The Computing Community Consortium (CCC) recently published a new report on how advanced computing, especially artificial intelligence (AI), can extend that impact even further while at the same time contribute to human-in-the-loop computational research. The report, titled Grand Challenges for the Convergence of Computational and Citizen Science Research, assesses the ways that such technology can increase the potential of citizen science, ultimately enhancing scientific capability more broadly. It also lays out the necessary next steps in computing research to make this convergence a reality.
Increasing Scientific Output
One of the biggest strengths of technology like AI is its ability to process and interpret large amounts of complex data. In addition to generating such datasets, citizen scientists possess the knowledge, creativity, and skills that make up for what such technology cannot do alone. They can enrich models with contextual insights and provide essential edge-case observations that increase the reliability of processed data, making the most of machine learning models while avoiding their downfalls.
This symbiotic relationship has the potential to support more accurate and timely scientific outputs across fields. For computing researchers specifically, it also creates a unique opportunity to do vital computational research into building trust, access, and transparency into AI systems by embedding them in real-world participatory contexts.
The applications of faster, more reliable data processing are broad. Other areas of scientific research that the report highlights as standing to benefit from convergence include supporting endangered species and protecting them from poachers, accelerating medical research, and improving our knowledge of astrophysics.
A Long-Term Research Roadmap
Further research into advanced computing technologies is required for convergence to truly flourish. Below are some of the foundational investigations required to advance the field, focusing on developing new models, metrics, and frameworks for human-AI interaction, trust, and accountability.
- Incentivize cross-disciplinary and cross-sectoral collaboration: Break down silos by investing in strategies that incentivize collaboration and knowledge sharing between computational and citizen science researchers. Encourage collaboration between academic researchers, federal agencies, industry, and local communities to co-design participatory platforms.
- Develop Human-AI teaming frameworks for public participation: Prototype and study new models for collaboration between AI and citizen scientists, particularly incorporating large language models. Specifically investigate novel systems where complementary roles are driven both by multi-agent decisions and community needs. Explore how these systems balance efficiency with human contextual insight in problems such as task assignment, anomaly detection, and data collection or labeling of large datasets.
- Develop explainable AI for non-expert users: Research novel user interface and data visualization techniques to make AI decision-making transparent and interpretable to the general public, as well as developers or researchers, enabling the general public to gain fluency with AI concepts and better understand how AI is used. Determine to what extent these techniques build trust and enable broader public engagement with AI-driven platforms.
- Design real-time feedback systems for citizen science: Research systems that enable adaptive, multilingual, and just-in-time digital feedback loops that guide users during data collection and analysis. This includes experimentation with LLMs, AR/VR, edge computing, and mobile-first design. Study these systems for improvement in data quality, user engagement, and learning outcomes.
- Institutionalize evaluation and trust metrics: Fund research on trust diagnostics, engagement dynamics, and societal benefit indicators to guide iterative improvement and accountability.
- Advance participatory AI governance models: Research mechanisms to ensure communities have real governance over how AI is deployed and data are used, including mechanisms for consent, opt-out, accountability, and oversight. Incentivize co-development of toolkits that help projects explain AI behavior to users, including uncertainty visualization, explainable model outputs, and citizen-led model critique workflows. Offer funding (e.g., challenge grants) for co-designed AI tools that are built with community organizations, encouraging broad public participation in scientific research.
- Pilot Human-AI teaming systems across multiple scales and domains: Fund research as well as deployment testbeds at local and global scales in domains where humans and AI collaborate in real time, including disaster response, health, and environmental monitoring, building scalable models for multi-agent systems.
Of course, the success of convergence also depends on the skills and preparedness of the humans involved — including researchers and citizen scientists themselves. Multilingual, psychology-informed, and adaptive systems incorporating AI that train and guide users must be developed and embedded in citizen science projects; such systems will build user capacity and skills while also improving data quality. And in continuing education, it’s essential to build both ML/AI skills and civic literacy, fostering a new generation of convergence-ready scientists, engineers, and public leaders.
Read the Full Report
For a full picture of the impact of large-scale convergence, as well as key recommendations across sectors for how to make it a reality, we encourage all members of the computing community to read the Grand Challenges for the Convergence of Computational and Citizen Science Research report below.
Read the Full Report Here
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