AI Snap! Blocks for Speech Input and Output, Computer Vision, Word Embeddings, and Neural Net Creation, Training, and Use

Source publication / research team or educational organization described in paper

China, India, UK

Researchers, educators, instructors, or facilitators as described in the source publication

• In-school (K-12)

Workshop / professional learning activity

Not specified in extracted text

We carried out a pilot program in India with AI Education courses curated for K-12 children. Scratch for AI blocks and Snap! for AI blocks were recently introduced to stu- dents in India to help them deduce and

Computer vision / image classification, NLP / text classification, ML concepts / supervised learning

• 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.
• Abstract We will demonstrate blocks integrated into Snap! capa- ble of a wide range of AI services, interactive AI pro- gramming guides, and a selection from thirty sample projects.

• 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, NLP / text classification, ML concepts / supervised learning

Language context discussed in source publication

• Learning object / concept model

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

• 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.
• Sessions and workshops in both school set- tings and informal learning contexts have been held in many countries.

• Sessions and workshops in both school set- tings and informal learning contexts have been held in many countries.
• AI library includes blocks that support word embeddings by reporting the embeddings, finding nearest words, and mapping words to two-dimensional locations in 15 languages (eCraft2learn 2021).

Abstract We will demonstrate blocks integrated into Snap! capa- ble of a wide range of AI services, interactive AI pro- gramming guides, and a selection from thirty sample projects. Sessions and workshops in both school set- tings and informal learning contexts have been held in many countries.

• 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.

• 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
• Assessment / feedback quality
• Learning tool / resource design
• Computer vision / image classification
• NLP / text classification
• ML concepts / supervised learning

• Completed

K-12

In-school (K-12)

Computer vision / image classification, NLP / text classification, ML concepts / supervised learning

Hands-on / experiential learning

Medium

High

AI Snap! Blocks for Speech Input and Output, Computer Vision, Word Embeddings, and Neural Net Creation, Training, and Use

• Ken Kahn
• Ramana Prasad
• Gayathri Veera

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36 No. 11, EAAI-22

2022

10.1609/aaai.v36i11.21568

https://ojs.aaai.org/index.php/AAAI/article/view/21568

https://ojs.aaai.org/index.php/AAAI/article/view/21568/21317

107_AI Snap! Blocks for Speech Input and Output, Computer Vision, Word Embeddings, and Neural Net Creation, Training, and Use.pdf

1

Abstract We will demonstrate blocks integrated into Snap! capa- ble of a wide range of AI services, interactive AI pro- gramming guides, and a selection from thirty sample projects. Sessions and workshops in both school set- tings and informal learning contexts have been held in many countries. The full version of this paper includes descriptions of the Snap! blocks and unpublished de- scriptions of student experiences in India. Summary1,2 The Snap! AI library includes blocks that support word embeddings by reporting the embeddings, finding nearest words, and mapping words to two-dimensional locations in 15 languages (eCraft2learn 2021). There are blocks that per- form image classification, object detection and segmenta- tion, pose detection, style transfer, sentence and image encoding, and audio recognition. TensorFlow.js is used to support neural network creation, training, prediction, and hyper-parameter optimization. These blocks run online or offline on the user’s devices thereby avoiding application installation, latency from network access, and possible privacy violations from data being sent to servers.

• 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.

Medium

• 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.