The loss of biodiversity and the growing presence of invasive non-native species pose a major challenge to local ecosystems. Understanding these issues requires the ability to observe living organisms, interpret data, and develop digital skills to use tools for data collection and identification. At the same time, the rapid development of AI technologies requires teaching learners a critical and informed use of these tools.

BioEduNum addresses these challenges with a teaching sequence that connects nature exploration, digital education, and citizen science. Learning research shows that contextualized outdoor activities using mobile technologies promote engagement and interaction with the environment (Land & Zimmerman, 2015). Citizen-science approaches offer an authentic framework to involve participants in a scientific investigation, enabling them to collect, use, and analyze real data. These approaches strengthen understanding of the role of data in science (National Academies of Sciences, Engineering, and Medicine, 2018).

Research questions:

1. How does a citizen-science activity promote positive epistemic emotions and affects (curiosity, motivation, engagement)?

2. How do different decision-support tools (AI, identification key) influence active nature observation?

3. How does context (urban/rural) affect digital skills and natural-science competencies?

BioEduNum adopts an experimental approach embedded in classrooms. Classrooms will be assigned to either the “self” or “other” condition. These labels refer to the type of data students will work with: either data they collected themselves (“self”) or data collected by other participants (“other”).

Participants in both conditions will go through two phases:

1. Observation and data collection phase (2 periods):
   The sequence begins with an observation phase introducing students to the topic of invasive species. They then explore a natural environment and collect data by taking geolocated photographs of plants encountered outdoors. Back in the classroom, they perform an initial identification of observed species using an AI-assisted tool.

2. Verification phase (2 periods):
   The sequence continues with an in-class verification phase where students check the AI’s identifications using an identification key. This step also prompts discussions about the capabilities, limits, and functioning of the AI.

The researchers will collaborate with biology experts to develop the AI designed to help students label the photos they collected. They will work with teachers to pilot the sequence before implementing it with a larger sample.

Emotions, affects, and knowledge regarding invasive species will be measured for all students before and after participation using self-report questionnaires and standardized tests.

In total, the team intends to recruit around ten classes from the canton of Vaud, grades 8H–9H (both VP and VG).

Through this study, the project aims to better understand how a teaching sequence that integrates nature exploration, the use of digital tools, and a citizen-science approach influences student learning. First, the researchers anticipate that participation in a citizen-science activity will foster positive epistemic emotions such as curiosity, interest, and engagement — possibly more so when students work with their own data, since people often value what they produced themselves. Second, they will investigate how decision-support tools affect nature observation: AI enables rapid but sometimes imprecise identifications, whereas an identification key requires finer observation and explicit taxonomic reasoning. The study seeks to determine to what extent these tools change the quality of observations and the understanding of species characteristics. Third, they  will analyze the effect of school context: by including classes from urban and rural environments, they aim to identify potential differences in digital competencies, science knowledge, and observation practices.

Finally, the project will produce resources for teachers. BioEduNum will provide:

• a ready-to-use, PER-aligned teaching sequence;

• an interactive platform for identification, verification, and visualization of observations;

• teaching resources (worksheets, slides, lesson plans).

In the long term, all resources will be made available via PER-MER, Switch-OER, and the Numériquement responsable platform.