<![CDATA[Citizen Science is a learning approach that involves students in scientific activities through direct data collection and analysis. In biology learning, especially biodiversity materials, the species identification process often faces challenges in the form of limited time and students' identification skills. This study aims to examine the application of generative AI-based citizen science learning models in outdoor biodiversity materials and evaluate the quality of the instruments used to measure students' perceptions of the implementation of the learning model. This study uses a quantitative approach involving 82 high school students in the Solo Raya area. Learning activities are carried out through field observation, plant identification using PlantNet, the use of generative AI to help the identification process, and collaborative preparation of observation reports. Data were obtained through questionnaires and analyzed using the Rasch Model through Winstep software to evaluate the validity and reliability of the instruments, as well as engaging taxonomists for data validation. The implementation of the learning model shows that the use of AI supports the process of plant identification, field data collection, collaboration, and report preparation. This research contributes to the application of a citizen science learning model that integrates generative AI as an alternative to biology teaching that is contextual and relevant to 21st century skills development.]]>
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