One of the oldest and largest biodiversity-related citizen science (CS) projects is eBird (https://ebird.org/home), developed by the Cornell Lab of Ornithology. It provides a mobile application for birdwatchers to record checklists of when, where, and how they have seen or heard birds. The Cornell Lab has also developed a mobile application, Merlin, which uses a deep convolutional neural network to help users automatically identify bird species from photos, sounds (converted to spectrograms), or descriptions. This research investigates how the use of machine learning (ML) classification models affects the learning of novice birders. Our participants (computer science students with no previous background in ornithology) were randomly divided into three groups: one using the eBird application and identifying bird species themselves; one using the Merlin application, which uses ML to automatically identify birds from photos or sounds; and a control group. Participants were tested on their knowledge of birds before and after participating in the project to see how using the ML classification model affected their learning. We also interviewed selected participants after the post-test to understand what they had done and what might explain the results. Our results show that novice participants who participate in a CS project for even a short time significantly improve their content knowledge of familiar birds in their neighbourhood, and that eBird users outperform Merlin users on the knowledge post-test. Although AI may improve volunteer productivity and retention, there is a risk that it may reduce their learning. Further research with different participant profiles and project designs is needed to understand how to optimise volunteer productivity, retention, and learning in AI-assisted CS projects.
