Scratch for Sports: Athletic Drills as a Platform for Experiencing, Understanding, and Developing AI-Driven Apps

Source publication / research team or educational organization described in paper

Not specified in extracted text

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

• In-school (K-12)

Curriculum design or implementation

Not specified in extracted text

Not specified in extracted text

Assessment / tutoring analytics

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

K-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.
• Culturally relevant and sustaining implementations of com- puting education are increasingly leveraging young learners' passion for sports as a platform for building interest in differ- ent STEM (Science, Technology, Engineering, and Math) concepts.

• 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.

Assessment / tutoring analytics

Not specified in extracted text

• Tutor

• Primary interaction pattern inferred from publication: Curriculum / course design, Learning tool / resource design, Assessment support, Outreach / informal learning.
• AI capability focus: Assessment / tutoring analytics.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• 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.

• Tutoring / feedback-supported 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: Tutoring / feedback-supported 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.
• Numerous disciplines spanning physics, engineer- ing, data science, and especially Artificial Intelligence (AI) based computing are not only authentically used in pro- fessional sports in today's world but can also be productively introduced to introduce young learners to these disciplines and facil

• Numerous disciplines spanning physics, engineer- ing, data science, and especially Artificial Intelligence (AI) based computing are not only authentically used in pro- fessional sports in today's world but can also be productively introduced to introduce young learners to these disciplines and facil
• In this work, we present a curriculum that includes a constellation of proprietary apps and tools that we show to student athletes learning sports like basketball and soccer which use AI methods like pose detection and IMU-based gesture detection to track activity and provide feedback.

Culturally relevant and sustaining implementations of com- puting education are increasingly leveraging young learners' passion for sports as a platform for building interest in differ- ent STEM (Science, Technology, Engineering, and Math) concepts. Numerous disciplines spanning physics, engineer- ing, data science, and especially Artificial Intelligence (AI) based computing are not only authentically used in pro- fessional sports in today's world but can also be productively introduced to introduce young learners to these disciplines and facilitate deep engagement with the same in the context of sports.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• 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
• Engagement / motivation
• Assessment / feedback quality
• Curriculum / course design
• Learning tool / resource design
• Assessment support
• Outreach / informal learning
• Assessment / tutoring analytics

• Completed

K-12

In-school (K-12)

Assessment / tutoring analytics

Tutoring / feedback-supported learning

Low to Medium

Medium

Scratch for Sports: Athletic Drills as a Platform for Experiencing, Understanding, and Developing AI-Driven Apps

• Vishesh Kumar
• Marcelo Worsley

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37 No. 13, EAAI-23

2023

10.1609/aaai.v37i13.26901

https://ojs.aaai.org/index.php/AAAI/article/view/26901

https://ojs.aaai.org/index.php/AAAI/article/view/26901/26673

092_Scratch for Sports_ Athletic Drills as a Platform for Experiencing, Understanding, and Developing AI-Driven Apps.pdf

6

Culturally relevant and sustaining implementations of com- puting education are increasingly leveraging young learners' passion for sports as a platform for building interest in differ- ent STEM (Science, Technology, Engineering, and Math) concepts. Numerous disciplines spanning physics, engineer- ing, data science, and especially Artificial Intelligence (AI) based computing are not only authentically used in pro- fessional sports in today's world but can also be productively introduced to introduce young learners to these disciplines and facilitate deep engagement with the same in the context of sports. In this work, we present a curriculum that includes a constellation of proprietary apps and tools that we show to student athletes learning sports like basketball and soccer which use AI methods like pose detection and IMU-based gesture detection to track activity and provide feedback. We also share Scratch extensions which enable rich access to sports related pose, object, and gesture detection algorithms that youth can then tinker around with and develop their own sports drill applications. We present early findings from pilot implementations of portions of these tools and curricula, which also fostered discussion relating to the failings, risks, and social harms associated with many of these different AI methods – noticeable in professional sports contexts, and rel- evant to youths' lives as active users of AI technologies as well as potential future creators of the same.

• 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

• group_size
• duration

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.