Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy
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...
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| Format: | Article |
| Language: | English |
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Ubiquity Press
2024-12-01
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| Series: | Citizen Science: Theory and Practice |
| Subjects: | |
| Online Access: | https://account.theoryandpractice.citizenscienceassociation.org/index.php/up-j-cstp/article/view/738 |
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| author | Carsten Østerlund Kevin Crowston Corey B. Jackson Yunan Wu Alexander O. Smith Aggelos K. Katsaggelos |
| author_facet | Carsten Østerlund Kevin Crowston Corey B. Jackson Yunan Wu Alexander O. Smith Aggelos K. Katsaggelos |
| author_sort | Carsten Østerlund |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c9a6cfaa029d4020acb0a43b42d1adc2 |
| institution | Kabale University |
| issn | 2057-4991 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | Citizen Science: Theory and Practice |
| spelling | doaj-art-c9a6cfaa029d4020acb0a43b42d1adc22025-01-08T07:54:40ZengUbiquity PressCitizen Science: Theory and Practice2057-49912024-12-0191424210.5334/cstp.738720Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity SpyCarsten Østerlund0https://orcid.org/0000-0003-0612-1551Kevin Crowston1https://orcid.org/0000-0003-1996-3600Corey B. Jackson2https://orcid.org/0000-0003-0828-4506Yunan Wu3https://orcid.org/0000-0001-6980-9746Alexander O. Smith4https://orcid.org/0000-0002-3719-2232Aggelos K. Katsaggelos5https://orcid.org/0000-0003-4554-0070Syracuse University, School of Information StudiesSyracuse UniversityUniversity of Wisconsin-Madison, School of Computer, Data, and Information SciencesNorthwestern University, Department of Electrical and Computer EngineeringSyracuse University, School of Information StudiesNorthwestern University, Department of Electrical and Computer EngineeringWe 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.https://account.theoryandpractice.citizenscienceassociation.org/index.php/up-j-cstp/article/view/738learningzone of proximal developmenthuman-artificial intelligence augmentation |
| spellingShingle | Carsten Østerlund Kevin Crowston Corey B. Jackson Yunan Wu Alexander O. Smith Aggelos K. Katsaggelos Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy Citizen Science: Theory and Practice learning zone of proximal development human-artificial intelligence augmentation |
| title | Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy |
| title_full | Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy |
| title_fullStr | Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy |
| title_full_unstemmed | Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy |
| title_short | Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy |
| title_sort | supporting human and machine co learning in citizen science lessons from gravity spy |
| topic | learning zone of proximal development human-artificial intelligence augmentation |
| url | https://account.theoryandpractice.citizenscienceassociation.org/index.php/up-j-cstp/article/view/738 |
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