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

Aug 6, 2026

Upload by Maeve

In this conceptual paper, existing literature on AI/ML education is reviewed to identify three approaches for learning and teaching machine learning in K-12. A data-driven approach supports youth in creating datasets and training/testing models. A learning-algorithm-driven approach emphasizes understanding learning algorithm internals. A third integrative approach combines both, while the review unpacks glassbox/blackbox perspectives, learner-interest-centered design opportunities, and the integration of ethics and justice, then outlines challenges, opportunities, and future tool and activity directions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

ML Education in K-12

Background

Three Approaches to Learning and Teaching ML

Data-driven approach

Learning algorithm-driven approach

Integrated approach

Unpacking Transparency, Design, Ethics, and Justice

Glassbox vs. blackbox

Learner interests and application design

Ethics and justice integration

Discussion and Future Directions

Unpacking Approaches to Learning and Teaching Machine Learning in K-12 Education: Transparency, Ethics, and Design Activities Luis Morales-Navarro Yasmin B. Kafai [luismn@upenn.edu](luismn@upenn.edu) [kafai@upenn.edu](kafai@upenn.edu) University of Pennsylvania Philadelphia, Pennsylvania, USA arXiv :2406 .03480v 3 [ cs .CY] 3 Sep 2024 ABSTRACT 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 learning algorithms. In addition, we identify efforts within a third approach that integrates the previous two. In our review, we focus on unpacking how the approaches: (1) glassbox and blackbox different aspects of ML,(2) build on learner interestsand 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 tools and learning activities. CCS CONCEPTS • Social and professional topics → K-12 education; Computing literacy. KEYWORDS machine learning, computing education, artificial intelligence, k-12, algorithmic justice, ethics ACM Reference Format: Luis Morales-Navarro and Yasmin B. Kafai. 2024. Unpacking Approaches to Learning and Teaching Machine Learning in K-12 Education: Transparency, Ethics, and Design Activities. In The 19th WiPSCE Conference on Primary and Secondary Computing Education Research (WiPSCE’24), September 16– 18, 2024, Munich, Germany. ACM, New York, NY, USA, 11 pages. [https:](https:)//[doi.org/10.1145/3677619.3678117](doi.org/10.1145/3677619.3678117) 1 INTRODUCTION While researchers have been investigating how to introduce young people to Artificial Intelligence/Machine Learning (AI/ML) ideas Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org). WiPSCE’24, September 16–18, 2024, Munich, Germany © 2024 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 979-8-4007-1005-6/24/09. . . $15.00 [https://doi.org/10.1145/3677619.3678117](https://doi.org/10.1145/3677619.3678117) for decades [23, 37], it is only during the last few years that AI/ML education has gained momentum [38] . This momentum has been a product of the comeback of machine learning methods, this time powered by increasing computing capacity and increasingly large datasets [85], the establishment of guidelines and principles [85], and the design of tools that enable novices to create models with small data sets [10, 16] . Today, in light of the popularization of large language models and generative models, young people interact with ML every day; governments call for increasing AI/ML education [13, 90], curriculum providers update their offerings [12], and teachers scramble to integrate AI/ML content in their classes. While principles, guidelines, and considerations serve as guiding frameworks for the design and implementation of learning activities, in practice, these may not always be enacted. Designing ML learning activities requires making decisions about what to glassbox and blackb

FAQs

What are the three approaches to learning and teaching machine learning in K-12 identified in the paper?

The paper identifies a data-driven approach, a learning-algorithm-driven approach, and an integrated approach that combines both.

How does the paper distinguish between glassbox and blackbox aspects of ML education?

It examines how different efforts choose what to make inspectable (glassbox) versus opaque (blackbox) when teaching machine learning concepts and practices.

How are ethics and justice addressed across the different ML education approaches?

The review focuses on how ethics and justice are integrated into the approaches and discusses their role when designing learning activities and tools.

Aug 6, 2026

Upload by Maeve