A No-Code AI Education Tool for Learning AI in K-12 by Making Machine Learning-Driven Apps
Pope, Nicolas; Vartiainen, Henriikka; Kahila, Juho; Laru, Jari; Tedre, Matti (2025-08-29)
IEEE
29.08.2025
N. Pope, H. Vartiainen, J. Kahila, J. Laru and M. Tedre, "A No-Code AI Education Tool for Learning AI in K-12 by Making Machine Learning-Driven Apps," 2024 IEEE International Conference on Advanced Learning Technologies (ICALT), Nicosia, North Cyprus, Cyprus, 2024, pp. 105-109, doi: 10.1109/ICALT61570.2024.00037.
https://rightsstatements.org/vocab/InC/1.0/
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202506275021
https://urn.fi/URN:NBN:fi:oulu-202506275021
Tiivistelmä
Abstract
This paper introduces an AI education tool designed for novice learners to create machine learning (classifier) based applications. Advancing from Google’s Teachable Machine 2 and developed using the design science research methodology, the tool is piloted in 36 K-12 classroom sessions with 213 children and allows learners to easily navigate the complete ML workflow—from data collection to app deployment—without any programming skills. To evaluate how well the tool met children’s expectations children were asked, as part of the design process, to articulate their goals and intentions for their apps; then, after using the tool, to describe how well they perceived their final app realized their intention. The tool’s main novelty is its ability to create a standalone app by defining one or more actions to be triggered by each classifier result, and deploy that app to other devices. A no-code approach and fully integrated development environment reduces the need for technical skills, making AI learning more inclusive. The tool represents a significant step in making AI education accessible for early learners, with future enhancements aimed at expanding its capabilities.
This paper introduces an AI education tool designed for novice learners to create machine learning (classifier) based applications. Advancing from Google’s Teachable Machine 2 and developed using the design science research methodology, the tool is piloted in 36 K-12 classroom sessions with 213 children and allows learners to easily navigate the complete ML workflow—from data collection to app deployment—without any programming skills. To evaluate how well the tool met children’s expectations children were asked, as part of the design process, to articulate their goals and intentions for their apps; then, after using the tool, to describe how well they perceived their final app realized their intention. The tool’s main novelty is its ability to create a standalone app by defining one or more actions to be triggered by each classifier result, and deploy that app to other devices. A no-code approach and fully integrated development environment reduces the need for technical skills, making AI learning more inclusive. The tool represents a significant step in making AI education accessible for early learners, with future enhancements aimed at expanding its capabilities.
Kokoelmat
- Avoin saatavuus [38841]