# ICE-T: A Multi-Faceted Concept for Teaching Machine Learning

arXiv: https://arxiv.org/abs/2411.05424 · HTML: https://mlsanity.com/paper/2411.05424/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 20 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [Addressing Trust in AI Systems through Education: A Didactic Perspective](https://mlsanity.com/paper/2609.02453/) (2026)

## Abstract (arXiv)

The topics of Artificial intelligence (AI) and especially Machine Learning (ML) are increasingly making their way into educational curricula. To facilitate the access for students, a variety of platforms, visual tools, and digital games are already being used to introduce ML concepts and strengthen the understanding of how AI works. We take a look at didactic principles that are employed for teaching computer science, define criteria, and, based on those, evaluate a selection of prominent existing platforms, tools, and games. Additionally, we criticize the approach of portraying ML mostly as a black-box and the resulting missing focus on creating an understanding of data, algorithms, and models that come with it. To tackle this issue, we present a concept that covers intermodal transfer, computational and explanatory thinking, ICE-T, as an extension of known didactic principles. With our multi-faceted concept, we believe that planners of learning units, creators of learning platforms and educators can improve on teaching ML.