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

TL;DR

This conceptual paper reviews existing literature on artificial intelligence/machine learning (AI/ML) education to identify three approaches to how learning and teaching ML could be conceptualized and addresses the challenges and opportunities of current approaches.

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 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.

Figures (2)

• Figure 1: Diagram of the 5 big ideas in AI education proposed by Touretzky et al. touretzky_envisioning_2019 and (in green box) details about what ML learning activities should promote touretzky2023machine.
• Figure 2: Distribution of studies by approach