News from the Teaching & Learning group!

The AI4LAM Teaching and Learning Working Group recently shared the draft of their review of AI teaching resources focused on GLAMs. They have (Nicole Coleman, Mike Trizna, Claudia Engel, Daniel Van Strien, Andrew Darby, Zachary Painter) just published a version on arxiv https://arxiv.org/abs/2205.04738

Beyond reviewing existing resources, they also made a few recommendations including “An emphasis on critical data practices and the ethical implications of Machine Learning is necessary” ML adds raises a bunch of new questions about how GLAMs manage, describe and share their collections, it’s important that considering these questions becomes a central focus. GLAM-specific training data will improve teaching and learning.” all data is unique but GLAM data can be even more unique! To learn to use ML for GLAM applications it's important to work with ‘real’ data. Effective implementation requires a focus on teaching the design and management of ML projects” it’s not just about training models but deciding where, when and how to use those models, or deciding to not use ML at all. They would still love feedback and are busy discussing the next steps. If you want to get involved in the working group please join the AI4LAM slack channel.