Visual tools for teaching machine learning in K-12: A ten-year systematic mapping
Open accessAbstract
Teaching Machine Learning in school helps students to be better prepared for a society rapidly changing due to the impact of Artificial Intelligence. This requires age-appropriate tools that allow students to develop a comprehensive understanding of Machine Learning in order to become creators of smart solutions. Following the trend of visual languages for introducing algorithms and programming in K-12, we present a ten-year systematic mapping of emerging visual tools that support the teaching of Machine Learning at this educational stage and analyze the tools concerning their educational characteristics, support for the development of ML models as well as their deployment and how the tools have been developed and evaluated. As a result, we encountered 16 tools targeting students mostly as part of short duration extracurricular activities. Tools mainly support the interactive development of ML models for image recognition tasks using supervised learning covering basic steps of the ML process. Being integrated into popular block-based programming languages (primarily Scratch and App Inventor), they also support the deployment of the created ML models as part of games or mobile applications. Findings indicate that the tools can effectively leverage students' understanding of Machine Learning, however, further studies regarding the design of the tools concerning educational aspects are required to better guide their effective adoption in schools and their enhancement to support the learning process more comprehensively.
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Some AI tools are integrated into block-based programming languages (such as Scratch or App Inventor), enabling the deployment of ML models built into games or mobile applications.
“Some of them are integrated into block-based programming languages (such as Scratch or App Inventor) (Toivonen et al., 2020; von Wangenheim et al., 2021), enabling the deployment of the ML models built into games or mobile applications.”
From AI literacy in K-12: a systematic literature review (2023) · cites this paper as von Wangenheim et al., 2021
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BackgroundMethodPedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review
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- Authors
- Gresse von Wangenheim, C., Hauck, J. C. R., Pacheco, F. S., & Bertonceli Bueno, M. F.
- Affiliations
- Universidade Federal de Santa Catarina (BR)
- Instituto Federal de Educação, Ciência e Tecnologia de Santa Catarina (BR)
- Venue
- Education and Information Technologies
- Published
- 2021-05-01
- Volume
- 26
- Issue
- 5
- Pages
- 5733-5778
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- PMC
- PMC8087535
- OpenAlex
- W3157682111
- Cited by
- 130
- References
- 85
- Citations by year
- 2026: 22 · 2025: 25 · 2024: 32 · 2023: 26 · 2022: 22 · 2021: 3
- FWCI
- 17.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Education and Information Technologies (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (303674/2019-9)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- machine learning education, K-12 education, visual programming tools, block-based programming, Scratch, MIT App Inventor, image recognition, supervised learning, extracurricular activities, mobile application deployment, AI literacy, educational tool evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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