A Versatile Low-Cost Kit for Teaching Novice Learners AI Using Robotics Components and a No-Code Development Playground

Source publication / research team or educational organization described in paper

Finland

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

the platforms above, we have developed an AI education platform designed for K– 12 children to learn ML concepts through hands-on robotics projects. The platform combines robotics with a “Teachable Machine” type

Robotics / physical AI

• 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.
• In the fast-growing field of K–12 AI education, there is an urgent need for accessible, hands-on tools that introduce AI concepts and workflows to novice learners.

• 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

Not specified in extracted text

• Automation tool

• Primary interaction pattern inferred from publication: Curriculum / course design, Learning tool / resource design, Physical AI / robotics learning.
• AI capability focus: Robotics / physical AI.

• 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.

• Hands-on / experiential 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: Hands-on / experiential 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 provide an alternative to existing AI education tools, this pa- per presents a low-cost robotics kit (<50C) designed to teach modern ML concepts through a no-code approach.

• To provide an alternative to existing AI education tools, this pa- per presents a low-cost robotics kit (<50C) designed to teach modern ML concepts through a no-code approach.
• The kit is grounded in maker pedagogy and designed for easy customiz- ability to different materials commonly found in classrooms, like cardboard, wood, metal, and plastic builder kits without the need for specialized tools.

In the fast-growing field of K–12 AI education, there is an urgent need for accessible, hands-on tools that introduce AI concepts and workflows to novice learners. In recent years, a variety of AI education tools have been introduced, rang- ing from coding environments to physical kits and robots.

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

• Activity documentation

• 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
• Learning tool / resource design
• Physical AI / robotics learning
• Robotics / physical AI

• Completed

K-12

In-school (K-12)

Robotics / physical AI

Hands-on / experiential learning

Low to Medium

Medium

A Versatile Low-Cost Kit for Teaching Novice Learners AI Using Robotics Components and a No-Code Development Playground

• Anssi Lin
• Anssi Salonen
• Nicolas Pope
• Henriikka Vartiainen
• Matti Tedre

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

2025

10.1609/aaai.v39i28.35189

https://ojs.aaai.org/index.php/AAAI/article/view/35189

https://ojs.aaai.org/index.php/AAAI/article/view/35189/37344

026_A Versatile Low-Cost Kit for Teaching Novice Learners AI Using Robotics Components and a No-Code Development Playground.pdf

9

In the fast-growing field of K–12 AI education, there is an urgent need for accessible, hands-on tools that introduce AI concepts and workflows to novice learners. In recent years, a variety of AI education tools have been introduced, rang- ing from coding environments to physical kits and robots. To provide an alternative to existing AI education tools, this pa- per presents a low-cost robotics kit (<50C) designed to teach modern ML concepts through a no-code approach. The kit is grounded in maker pedagogy and designed for easy customiz- ability to different materials commonly found in classrooms, like cardboard, wood, metal, and plastic builder kits without the need for specialized tools. For programming the robot’s actions, the kit features an all-in-one development studio that is compatible with most phone, laptop, and tablet platforms and can operate with or without an Internet connection, mak- ing it applicable to a wide range of educational contexts, in- cluding ICT4D.

• 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

• 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.