An Introduction to Rule-Based Feature and Object Perception for Middle 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)

Classroom, course, or resource-based AI education activity

Not specified in extracted text

”1 AI4K12 details several related concepts and learning objectives that build K-12 students’ understanding of how AI extracts meaning from sensory data through com- puter vision. These include understanding the; ithms for tasks like computer vision. Several tools are already used to teach K-12 students about various aspects of computer vision. For example, Teachable Machine allows K-12 students to create their own image; out various aspects of computer vision. For example, Teachable Machine allows K-12 students to create their own image classification models using supervised machine learn- ing (Carney et al. 2020). While users a

Computer vision / image classification, ML concepts / supervised learning

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

6-8

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.
• The Feature Detection tool is a web-based activity that allows students to detect features in images and build their own rule- based classification algorithms.

• 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, ML concepts / supervised learning

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Instructional design / AI education.
• AI capability focus: Computer vision / image classification, ML concepts / supervised learning.

• 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.
• The second lesson aims to show students how these lower-level features can be incorporated into rule-based models to clas- sify higher-order objects.

• The second lesson aims to show students how these lower-level features can be incorporated into rule-based models to clas- sify higher-order objects.

The Feature Detection tool is a web-based activity that allows students to detect features in images and build their own rule- based classification algorithms. In this paper, we introduce the tool and share how it is incorporated into two, 45-minute lessons.

• 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
• Instructional design / AI education
• Computer vision / image classification
• ML concepts / supervised learning

• Completed

6-8

In-school (K-12)

Computer vision / image classification, ML concepts / supervised learning

Instructional / curriculum-based learning

Medium

High

An Introduction to Rule-Based Feature and Object Perception for Middle School Students

• Daniella DiPaola
• Parker Malachowsky
• Nancye Blair Black
• Sharifa Alghowinem
• Xiaoxue Du
• Cynthia Breazeal

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

2023

10.1609/aaai.v37i13.26900

https://ojs.aaai.org/index.php/AAAI/article/view/26900

https://ojs.aaai.org/index.php/AAAI/article/view/26900/26672

091_An Introduction to Rule-Based Feature and Object Perception for Middle School Students.pdf

7

The Feature Detection tool is a web-based activity that allows students to detect features in images and build their own rule- based classification algorithms. In this paper, we introduce the tool and share how it is incorporated into two, 45-minute lessons. The objective of the first lesson is to introduce stu- dents to the concept of feature detection, or how a computer can break down visual input into lower-level features. The second lesson aims to show students how these lower-level features can be incorporated into rule-based models to clas- sify higher-order objects. We discuss how this tool can be used as a ”first step” to the more complex concept ideas of data representation and neural networks. Figure 1: Welcome screen of the Feature Detection Tool

• 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.