Beyond Black-Boxes: Teaching Complex Machine Learning Ideas through Scaffolded Interactive Activities

Source publication / research team or educational organization described in paper

Not specified in extracted text

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

rch on appropriate strategies and pedago- gies for teaching ML, especially to K-12 students is still evolving with little clarity on how—and at what depth— to teach these complex ideas to younger learners (Evang

ML concepts / supervised learning, Explainable AI / robustness

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• 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.
• Existing approaches to teaching artificial intelligence and ma- chine learning (ML) often focus on the use of pre-trained models or fine-tuning an existing black-box architecture.

• 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.

ML concepts / supervised learning, Explainable AI / robustness

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design, Teacher professional development.
• AI capability focus: ML concepts / supervised learning, Explainable AI / robustness.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• 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, Hands-on / experiential learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• 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, Hands-on / experiential 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.
• We believe ML techniques and core ML topics, such as opti- mization and adversarial examples, can be designed for high school age students given appropriate support.

• We believe ML techniques and core ML topics, such as opti- mization and adversarial examples, can be designed for high school age students given appropriate support.

Existing approaches to teaching artificial intelligence and ma- chine 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 opti- mization and adversarial examples, can be designed for high school age students given appropriate support.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• 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
• Curriculum / course design
• Teacher professional development
• ML concepts / supervised learning
• Explainable AI / robustness

• Completed

9-12

In-school (K-12)

ML concepts / supervised learning, Explainable AI / robustness

Game-based learning, Hands-on / experiential learning

Low to Medium

Medium

Beyond Black-Boxes: Teaching Complex Machine Learning Ideas through Scaffolded Interactive Activities

• Brian Broll
• Shuchi Grover

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37 No. 13, EAAI-23

2023

10.1609/aaai.v37i13.26898

https://ojs.aaai.org/index.php/AAAI/article/view/26898

https://ojs.aaai.org/index.php/AAAI/article/view/26898/26670

089_Beyond Black-Boxes_ Teaching Complex ML Ideas through Scaffolded Interactive Activities.pdf

9

Existing approaches to teaching artificial intelligence and ma- chine 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 opti- mization 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 pro- gramming 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 optimiza- tion and inspection. This paper describes our AI & Cyberse- curity for Teens suite of curricular activities aimed at high school students and teachers.

• In-school (K-12)

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• 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.