# Exploring Machine Teaching with Children

arXiv: https://arxiv.org/abs/2109.11434 · HTML: https://mlsanity.com/paper/2109.11434/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 66 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [An Overview of Machine Teaching](https://mlsanity.com/paper/1801.05927/) (2018)
- [Diversity in Machine Learning](https://mlsanity.com/paper/1807.01477/) (2018)
- [Machine Teaching: A New Paradigm for Building Machine Learning Systems](https://mlsanity.com/paper/1707.06742/) (2017)
- [Crowdsourcing the Perception of Machine Teaching](https://mlsanity.com/paper/2002.01618/) (2020)
- [Scratch Community Blocks: Supporting Children as Data Scientists](https://mlsanity.com/paper/1702.00112/) (2017)

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

- [Blind Users Accessing Their Training Images in Teachable Object Recognizers](https://mlsanity.com/paper/2208.07968/) (2022)
- [MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech](https://mlsanity.com/paper/2204.00145/) (2022)
- [Collaborative Machine Learning Model Building with Families Using Co-ML](https://mlsanity.com/paper/2304.05444/) (2023)
- [Examining the Values Reflected by Children during AI Problem Formulation](https://mlsanity.com/paper/2309.15839/) (2023)

## Abstract (arXiv)

Iteratively building and testing machine learning models can help children develop creativity, flexibility, and comfort with machine learning and artificial intelligence. We explore how children use machine teaching interfaces with a team of 14 children (aged 7-13 years) and adult co-designers. Children trained image classifiers and tested each other's models for robustness. Our study illuminates how children reason about ML concepts, offering these insights for designing machine teaching experiences for children: (i) ML metrics (e.g. confidence scores) should be visible for experimentation; (ii) ML activities should enable children to exchange models for promoting reflection and pattern recognition; and (iii) the interface should allow quick data inspection (e.g. images vs. gestures).