FOLL-E: Teaching First Order Logic to Children

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

Computer vision / image classification

• 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.
• First-order logic (FO) is an important foundation of many do- mains, including computer science and artificial intelligence.

• 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

Language context discussed in source publication

• Learning object / concept model

• Primary interaction pattern inferred from publication: Learning tool / resource design, Outreach / informal learning.
• AI capability focus: Computer vision / image classification.

• 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.

• 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.
• In this paper, we ex- amine whether it is possible to design a learning environ- ment that both motivates and enables children to learn the ba- sics of FO.

• In this paper, we ex- amine whether it is possible to design a learning environ- ment that both motivates and enables children to learn the ba- sics of FO.
• The key components of the learning environment are a syntax-free blocks-based notation for FO, graphics- based puzzles to solve, and a tactile environment which uses computer vision to allow the children to work with wooden blocks.
• The resulting FOLL-E system is intended to sharpen childrens’ reasoning skills, encourage critical thinking and make them aware of the ambiguities of natural language.

First-order logic (FO) is an important foundation of many do- mains, including computer science and artificial intelligence. In recent efforts to teach basic CS and AI concepts to chil- dren, FO has so far remained absent.

• 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
• Learning tool / resource design
• Outreach / informal learning
• Computer vision / image classification

• Completed

K-12

In-school (K-12)

Computer vision / image classification

Instructional / curriculum-based learning

Low to Medium

Low to Medium

FOLL-E: Teaching First Order Logic to Children

• Simon Vandevelde
• Joost Vennekens

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

2023

10.1609/aaai.v37i13.26884

https://ojs.aaai.org/index.php/AAAI/article/view/26884

https://ojs.aaai.org/index.php/AAAI/article/view/26884/26656

075_FOLL-E_ Teaching First Order Logic to Children.pdf

8

First-order logic (FO) is an important foundation of many do- mains, including computer science and artificial intelligence. In recent efforts to teach basic CS and AI concepts to chil- dren, FO has so far remained absent. In this paper, we ex- amine whether it is possible to design a learning environ- ment that both motivates and enables children to learn the ba- sics of FO. The key components of the learning environment are a syntax-free blocks-based notation for FO, graphics- based puzzles to solve, and a tactile environment which uses computer vision to allow the children to work with wooden blocks. The resulting FOLL-E system is intended to sharpen childrens’ reasoning skills, encourage critical thinking and make them aware of the ambiguities of natural language. Dur- ing preliminary testing with children, they reported that they found the notation intuitive and inviting, and that they en- joyed interacting with the application.

• 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.