Beyond Black-Boxes: Teaching Complex Machine Learning Ideas through Scaffolded Interactive Activities
Keywords:Artificial Intelligence, K-12 AI Education, Machine Learning, Scaffolding
AbstractExisting approaches to teaching artificial intelligence and machine learning (ML) often focus on the use of pre-trained models or fine-tuning an existing black-box architecture. We believe ML techniques and core ML topics, such as optimization and adversarial examples, can be designed for high school age students given appropriate support. Our curricular approach focuses on teaching ML ideas by enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. Then, students are able to engage with the concepts via meaningful, hands-on experiences that span the entire ML process from data collection to model optimization and inspection. This paper describes our 'AI & Cybersecurity for Teens' suite of curricular activities aimed at high school students and teachers.
How to Cite
Broll, B., & Grover, S. (2023). Beyond Black-Boxes: Teaching Complex Machine Learning Ideas through Scaffolded Interactive Activities. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15990-15998. https://doi.org/10.1609/aaai.v37i13.26898
EAAI Symposium: Resources for Teaching AI in K-12