# Unpacking Approaches to Learning and Teaching Machine Learning in K-12 Education: Transparency, Ethics, and Design Activities

arXiv: https://arxiv.org/abs/2406.03480 · HTML: https://mlsanity.com/paper/2406.03480/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [Scratch Copilot: Supporting Youth Creative Coding with AI](https://mlsanity.com/paper/2505.03867/) (2025)
- [Learning About Algorithm Auditing in Five Steps: Scaffolding How High School Youth Can Systematically and Critically Evaluate Machine Learning Applications](https://mlsanity.com/paper/2412.06989/) (2024)
- [DynaStride: Dynamic Stride Windowing with MMCoT for Instructional Multi-Scene Captioning](https://mlsanity.com/paper/2510.23907/) (2025)
- [Addressing Trust in AI Systems through Education: A Didactic Perspective](https://mlsanity.com/paper/2609.02453/) (2026)

## Abstract (arXiv)

In this conceptual paper, we review existing literature on artificial intelligence/machine learning (AI/ML) education to identify three approaches to how learning and teaching ML could be conceptualized. One of them, a data-driven approach, emphasizes providing young people with opportunities to create data sets, train, and test models. A second approach, learning algorithm-driven, prioritizes learning about how the learning algorithms or engines behind how ML models work. In addition, we identify efforts within a third approach that integrates the previous two. In our review, we focus on how the approaches: (1) glassbox and blackbox different aspects of ML, (2) build on learner interests and provide opportunities for designing applications, (3) integrate ethics and justice. In the discussion, we address the challenges and opportunities of current approaches and suggest future directions for the design of learning activities.