Smartphone-Based Game Development to Introduce K12 Students in Applied Artificial Intelligence

Source publication / research team or educational organization described in paper

Finland, Italy

Researchers, educators, instructors, or facilitators as described in the source publication

• In-school (K-12)

Curriculum design or implementation

8 hours

is paper presents a structured activity based on a game de- sign to introduce k-12 students in the topic of super-vised ma- chine learning from a practical perspective. The activity has been developed in the sco

Computer vision / image classification, ML concepts / supervised learning

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

9-12

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• This paper presents a structured activity based on a game de- sign to introduce k-12 students in the topic of super-vised ma- chine learning from a practical perspective.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

Computer vision / image classification, ML concepts / supervised learning

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design.
• AI capability focus: Computer vision / image classification, ML concepts / supervised learning.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Game-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Game-based learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• From a didactic perspective, the stu- dents dealt with supervised learning to solve a problem of im- age classification.

• From a didactic perspective, the stu- dents dealt with supervised learning to solve a problem of im- age classification.

This paper presents a structured activity based on a game de- sign to introduce k-12 students in the topic of super-vised ma- chine learning from a practical perspective. The activity has been developed in the scope of an Erasmus+ project called AI+, which aims to develop an AI curriculum for high school students.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.

• Design / conceptual evidence

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• Conceptual understanding
• Curriculum / course design
• Computer vision / image classification
• ML concepts / supervised learning

• Completed

9-12

In-school (K-12)

Computer vision / image classification, ML concepts / supervised learning

Game-based learning

Medium

High

Smartphone-Based Game Development to Introduce K12 Students in Applied Artificial Intelligence

• Sara Guerreiro-Santalla
• Alma Mallo
• Tamara Baamonde
• Francisco Bellas

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36 No. 11, EAAI-22

2022

10.1609/aaai.v36i11.21554

https://ojs.aaai.org/index.php/AAAI/article/view/21554

https://ojs.aaai.org/index.php/AAAI/article/view/21554/21303

101_Smartphone-Based Game Development to Introduce K12 Students in Applied Artificial Intelligence.pdf

8

This paper presents a structured activity based on a game de- sign to introduce k-12 students in the topic of super-vised ma- chine learning from a practical perspective. The activity has been developed in the scope of an Erasmus+ project called AI+, which aims to develop an AI curriculum for high school students. As established in the AI+ principles, all the teaching activities are based on the use of the student's smartphone as the core element to intro-duce an applied approach to AI in classes. In this case, a smartphone-based game app is devel- oped by students that includes a neural network model ob- tained with the "Personal Image Classifier" tool of the MIT App Inventor software. From a didactic perspective, the stu- dents dealt with supervised learning to solve a problem of im- age classification. The main learning outcome is the under- standing of how relevant is to develop a reliable machine learning model when dealing with real world applications. This activity was tested during 2021 with more than 50 stu- dents belonging to six schools across Europe, all of them en- rolled in the AI+ project.

• In-school (K-12)

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.