Introducing Variational Autoencoders to High School Students

TL;DR

This paper presents a novel educational approach to teaching high school students about Variational Autoencoders (VAEs) using interactive tools, philosophical metaphors, and hands-on activities to enhance understanding of complex AI concepts.

Contribution

The paper introduces a new curriculum and teaching methods for explaining VAEs to high school students, integrating art, philosophy, and practical exercises.

Findings

Students gained a clear understanding of VAEs.

The approach was effective in engaging students with complex AI concepts.

Pilot studies showed positive learning outcomes.

Abstract

Generative Artificial Intelligence (AI) models are a compelling way to introduce K-12 students to AI education using an artistic medium, and hence have drawn attention from K-12 AI educators. Previous Creative AI curricula mainly focus on Generative Adversarial Networks (GANs) while paying less attention to Autoregressive Models, Variational Autoencoders (VAEs), or other generative models, which have since become common in the field of generative AI. VAEs' latent-space structure and interpolation ability could effectively ground the interdisciplinary learning of AI, creative arts, and philosophy. Thus, we designed a lesson to teach high school students about VAEs. We developed a web-based game and used Plato's cave, a philosophical metaphor, to introduce how VAEs work. We used a Google Colab notebook for students to re-train VAEs with their hand-written digits to consolidate their understandings. Finally, we guided the exploration of creative VAE tools such as SketchRNN and MusicVAE to draw the connection between what they learned and real-world applications. This paper describes the lesson design and shares insights from the pilot studies with 22 students. We found that our approach was effective in teaching students about a novel AI concept.