Review of GLAM Training Resources

The AI4LAM Teaching and Learning working group was established in August 2020. Per the Charter the purpose of our group is to evaluate existing training needs in GLAMs and how they are currently met. We set out to explore pedagogical approaches and delivery mechanisms and to review available resources for teaching and learning AI. Over the past year we conducted a survey of existing training opportunities and generated a summary report about our findings.

We gathered and reviewed a total of 28 resources. We summarize our findings in the following main points:

• There is a need for machine learning concepts (without coding).

• Where coding is necessary, Python is important. 

• Community building and project-based work will improve learning.

• Maintenance and discovery of training materials for the GLAM sector will benefit from community organizing.

• An emphasis on critical data practices and the ethical implications of Machine Learning is necessary.

• GLAM-specific training data will improve teaching and learning. 

• Effective implementation requires a focus on teaching the design and management of ML projects

We would love to hear your thoughts and invite the ai4lam community for comment. Please share widely and provide feedback to our review, either directly in the Google document or via our ai4lam Slack channel #teaching-and-learning-ai. If there are learning resources we should consider, please add them here.

If you are interested in joining the working group, please see our meeting notes for details. Our group is open to anyone from the GLAM community interested in AI.

Links:

Training sources review

Training resources

Teaching and Learning Working Group charter

Teaching and Learning Working Group monthly meeting notes 

ai4lam Slack (Slack channel: #teaching-and-learning-ai)

Presentation for ai4lam community call