A Humanitarians AI project
A browser-based learning and conservation initiative that turns the common loon's annual cycle into interactive stories. Each experience connects to real field research on the Whitefish Chain.
See LoonNet in action
Watch a short introduction to the four interactive chapters and the conservation ideas that connect each experience to the common loon's annual cycle.
Watch on YouTube ↗Each short chapter turns one biological fact into a playable consequence, from choosing a spring nest site to making the fall takeoff. Play in your browser with no installation required.
Developed based on concepts and chapters from Generative AI for Game Development: Crafting Narrative Worlds with Machines by Shesh Narayan Gupta · DOI: 10.1007/979-8-8688-2439-5
LoonNet connects browser-based learning with conservation work: interactive chapters paired with seasonal drone, acoustic, and underwater field capture, plus practical tools for annotation, loon detection and counting, habitat-change review, and responsible sharing.
Visitors learn why nesting height, water clarity, boat wakes, and open water matter by making decisions rather than reading labels alone.
Whitefish Chain footage and annotated imagery connect the public-facing experience to the actual lakes, birds, habitats, and seasonal conditions.
Usable, reviewable tools for non-technical teams, with transparent results and human expertise kept in the decision loop.
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