Smart Motor: A Low-Cost Hardware and Software Toolkit for Introducing Supervised Machine Learning to Elementary School Students

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)

Tool / platform-supported learning activity

Not specified in extracted text

Not specified in extracted text

Robotics / physical AI, ML concepts / supervised learning

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

K-5

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.
• With the rise of Artificial Intelligence (AI) systems in society, our children have routine interactions with these technolo- gies.

• 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.

Robotics / physical AI, ML concepts / supervised learning

Not specified in extracted text

• Automation tool

• Primary interaction pattern inferred from publication: Learning tool / resource design, Outreach / informal learning, Physical AI / robotics learning.
• AI capability focus: Robotics / physical AI, ML concepts / supervised learning.

• 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.

• Instructional / curriculum-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: Instructional / curriculum-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.
• To introduce children to AI and Machine Learning (ML) concepts, recent efforts intro- duce tools that integrate ML concepts with physical com- puting and robotics.

• To introduce children to AI and Machine Learning (ML) concepts, recent efforts intro- duce tools that integrate ML concepts with physical com- puting and robotics.
• We ad- dress these limitations by offering a low-cost hardware and software toolkit that we call the Smart Motor to introduce supervised machine learning to elementary school students.

With the rise of Artificial Intelligence (AI) systems in society, our children have routine interactions with these technolo- gies. It has become increasingly important for them to un- derstand how these technologies are trained, what their lim- itations are and how they work.

• 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
• Learning tool / resource design
• Outreach / informal learning
• Physical AI / robotics learning
• Robotics / physical AI
• ML concepts / supervised learning

• Completed

K-5

In-school (K-12)

Robotics / physical AI, ML concepts / supervised learning

Instructional / curriculum-based learning

Low to Medium

Medium

Smart Motor: A Low-Cost Hardware and Software Toolkit for Introducing Supervised Machine Learning to Elementary School Students

• Tanushree Burman
• Milan Dahal
• Geling Xu
• Chris Rogers
• Jennifer Cross
• Jivko Sinapov

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35185

https://ojs.aaai.org/index.php/AAAI/article/view/35185

https://ojs.aaai.org/index.php/AAAI/article/view/35185/37340

022_Smart Motor_ A Low-Cost Hardware and Software Toolkit for Introducing Supervised Machine Learning to Elementary School Students.pdf

9

With the rise of Artificial Intelligence (AI) systems in society, our children have routine interactions with these technolo- gies. It has become increasingly important for them to un- derstand how these technologies are trained, what their lim- itations are and how they work. To introduce children to AI and Machine Learning (ML) concepts, recent efforts intro- duce tools that integrate ML concepts with physical com- puting and robotics. However, some of these tools cannot be easily integrated into building projects and the high price of robotics kits can be a limiting factor to many schools. We ad- dress these limitations by offering a low-cost hardware and software toolkit that we call the Smart Motor to introduce supervised machine learning to elementary school students. Our Smart Motor uses the nearest neighbor algorithm and utilizes visualizations to highlight the underlying decision- making of the model. We conducted a one week long study using Smart Motors with 9- to 12- year old students and mea- sured their learning through observation, questioning and ex- amining what they built. We found that students were able to integrate the Smart Motors into their building projects but some students struggled with understanding how the underly- ing model functioned. In this paper we discuss these findings and insights for future directions for the Smart Motor. Smart Motor — https://smartmotors.notion.site/ Smart App — https://smart-motors.web.app/

• 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.