Mutual learning in human-AI interaction

Publication Type:

Conference Paper


Trust and Reliance in Evolving Human-AI Workflows (TREW) Workshop, ACM CHI Conference, Honolulu, HI (2024)


<p>We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the Zone of Proximal Development concept, which allows us to describe the augmentation of human learning by AI, human augmentation of machine learning and how tasks can be designed to facilitate co-augmentation. Methodologically, the study utilizes 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-augmentation, where both humans and machines contribute to each other’s learning and capabilities.&nbsp; The research contributes to the existing literature by emphasizing the role of ZPD in citizen science projects, showcasing how the concept supports ongoing learning for volunteers and keeps machine learning aligned with evolving data.</p>

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