“AlphAI”: Teaching AI Algorithms to K12 by Training Learning Robots and Visualizing How It Works

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)
• Higher education

Classroom, course, or resource-based AI education activity

2 hours!

e based on intuitive concepts that can be explained to non-experts, including K-12 students. Numer- ous popularization videos (e.g., (3Blue1Brown 2017)) and web animations (Karpathy 2014; Tensorflow Playground); - vised learning, and there are fewer initiatives aimed at ex- plaining it to K-12 students (though see (Zhang et al. 2022)). Nonetheless, it presents a powerful approach to tackle the fundamental questions of h

Robotics / physical AI, ML concepts / supervised learning

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

K-5, Higher education

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.
• Given the massive transformation of all areas of society by AI, it is becoming essential to integrate AI literacy into the various school curricula from an early age.

• 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.
• However, teaching the basic concepts of AI and Machine Learning (e.g. training a model; artificial neural networks) at the K-12 level might seem too abstract, whereas teaching only how to use AI fails to really ”open the black box”.

• However, teaching the basic concepts of AI and Machine Learning (e.g. training a model; artificial neural networks) at the K-12 level might seem too abstract, whereas teaching only how to use AI fails to really ”open the black box”.
• This is achieved by making AI very concrete, first by manipulating the learning of educa- tional robots that users train for different behaviors, such as circuit racing, using either supervised or reinforcement learn- ing; second by visualizing in real time in a graphical inter- face the details of

Given the massive transformation of all areas of society by AI, it is becoming essential to integrate AI literacy into the various school curricula from an early age. However, teaching the basic concepts of AI and Machine Learning (e.g. training a model; artificial neural networks) at the K-12 level might seem too abstract, whereas teaching only how to use AI fails to really ”open the black box”.

• 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.

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

• Completed

K-5, Higher education

In-school (K-12), Higher education

Robotics / physical AI, ML concepts / supervised learning

Instructional / curriculum-based learning

Low to Medium

Low to Medium

“AlphAI”: Teaching AI Algorithms to K12 by Training Learning Robots and Visualizing How It Works

• Marie Absalon
• Thomas Deneux

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

2025

10.1609/aaai.v39i28.35181

https://ojs.aaai.org/index.php/AAAI/article/view/35181

https://ojs.aaai.org/index.php/AAAI/article/view/35181/37336

018_#U201cAlphAI#U201d_ Teaching AI Algorithms to K12 by Training Learning Robots and Visualizing How It Works.pdf

8

Given the massive transformation of all areas of society by AI, it is becoming essential to integrate AI literacy into the various school curricula from an early age. However, teaching the basic concepts of AI and Machine Learning (e.g. training a model; artificial neural networks) at the K-12 level might seem too abstract, whereas teaching only how to use AI fails to really ”open the black box”. To overcome these difficulties, we have developed AlphAI, a software resource designed to make the understanding of AI algorithms accessible and attractive to the general public and children as young as 8 years old. This is achieved by making AI very concrete, first by manipulating the learning of educa- tional robots that users train for different behaviors, such as circuit racing, using either supervised or reinforcement learn- ing; second by visualizing in real time in a graphical inter- face the details of AI algorithms (neural networks, k-nearest neighbors, Q-learning, etc). In addition, the use of the soft- ware is not limited to beginners, since it allows to write one’s own AI in Python to control the robots. In this paper, we present the basic principles of the software, its graphical interface, how to use it with various educational robots, and example activities with classes from Elementary school to University. AlphAI software and robotic kits are commercially available from Learning Robots.

• In-school (K-12)
• Higher education

• 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.