Introducing Variational Autoencoders to High School Students

Source publication / research team or educational organization described in paper

United States, Canada

Researchers, educators, instructors, or facilitators as described in the source publication

• In-school (K-12)

Classroom, course, or resource-based AI education activity

Not specified in extracted text

rative Artificial Intelligence (AI) models are a com- pelling way to introduce K-12 students to AI education us- ing an artistic medium, and hence have drawn attention from K-12 AI educators. Previous Creative AI; udents re-train VAEs with hand-written digits. Findings from pilot studies with 22 participants indicate students understood the role of the encoder and decoder after the lesson and enjoyed the hands-on experience o; tencourt 2020), but those require mathematics and statistics foundations that K-12 students do not have. In our lesson, we focused on intuition and applica- tions instead. VAEs as a subgroup of generative models

Generative AI

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

9-12

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• Generative Artificial Intelligence (AI) models are a com- pelling way to introduce K-12 students to AI education us- ing an artistic medium, and hence have drawn attention from K-12 AI educators.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

Generative AI

Not specified in extracted text

• Co-creator

• Primary interaction pattern inferred from publication: Teacher professional development.
• AI capability focus: Generative AI.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Game-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Game-based learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• VAEs’ latent-space structure and interpolation ability could effec- tively ground the interdisciplinary learning of AI, creative arts, and philosophy.

• VAEs’ latent-space structure and interpolation ability could effec- tively ground the interdisciplinary learning of AI, creative arts, and philosophy.

Generative Artificial Intelligence (AI) models are a com- pelling way to introduce K-12 students to AI education us- ing an artistic medium, and hence have drawn attention from K-12 AI educators. Previous Creative AI curricula mainly fo- cus on Generative Adversarial Networks (GANs) while pay- ing less attention to Autoregressive Models, Variational Au- toencoders (VAEs), or other generative models, which have since become common in the field of generative AI.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.

• Design / conceptual evidence

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• Conceptual understanding
• Teacher readiness
• Teacher professional development
• Generative AI

• Completed

9-12

In-school (K-12)

Generative AI

Game-based learning

Medium

Medium

Introducing Variational Autoencoders to High School Students

• Zhuoyue Lyu
• Safinah Ali
• Cynthia Breazeal

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36 No. 11, EAAI-22

2022

10.1609/aaai.v36i11.21559

https://ojs.aaai.org/index.php/AAAI/article/view/21559

https://ojs.aaai.org/index.php/AAAI/article/view/21559/21308

103_Introducing Variational Autoencoders to High School Students.pdf

9

Generative Artificial Intelligence (AI) models are a com- pelling way to introduce K-12 students to AI education us- ing an artistic medium, and hence have drawn attention from K-12 AI educators. Previous Creative AI curricula mainly fo- cus on Generative Adversarial Networks (GANs) while pay- ing less attention to Autoregressive Models, Variational Au- toencoders (VAEs), or other generative models, which have since become common in the field of generative AI. VAEs’ latent-space structure and interpolation ability could effec- tively 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 in- troduce 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 ex- ploration of creative VAE tools such as SketchRNN and Mu- sicVAE 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 stu- dents. We found that our approach was effective in teaching students about a novel AI concept.

• In-school (K-12)

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.