StoryQ—an Online Environment for Machine Learning of Text Classification

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

Tool / platform-supported learning activity

Not specified in extracted text

Not specified in extracted text

NLP / text classification, ML concepts / supervised learning

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

K-12, 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.
• StoryQ—an Online Environment for Machine Learning of Text Classification William Finzer1, Jie Chao1, Carolyn Rose2 and Shiyan Jiang3 The Concord Consortium1, Carnegie Mellon University2 and North Carolina State University3

• 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.

NLP / text classification, ML concepts / supervised learning

Not specified in extracted text

• Co-creator

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

• No specific learning outcome sentence was automatically extracted from the abstract; manual review recommended.

StoryQ—an Online Environment for Machine Learning of Text Classification William Finzer1, Jie Chao1, Carolyn Rose2 and Shiyan Jiang3 The Concord Consortium1, Carnegie Mellon University2 and North Carolina State University3

• 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.

• Conceptual understanding
• Learning tool / resource design
• NLP / text classification
• ML concepts / supervised learning

• Completed

K-12, Higher education

In-school (K-12), Higher education

NLP / text classification, ML concepts / supervised learning

Instructional / curriculum-based learning

Medium

Low to Medium

StoryQ—an Online Environment for Machine Learning of Text Classification

• William Finzer
• Jie Chao
• Carolyn Rose
• Shiyan Jiang

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

2022

10.1609/aaai.v36i11.21567

https://ojs.aaai.org/index.php/AAAI/article/view/21567

https://ojs.aaai.org/index.php/AAAI/article/view/21567/21316

106_StoryQ_ An Online Environment for Machine Learning of Text Classification.pdf

1

StoryQ—an Online Environment for Machine Learning of Text Classification William Finzer1, Jie Chao1, Carolyn Rose2 and Shiyan Jiang3 The Concord Consortium1, Carnegie Mellon University2 and North Carolina State University3

• 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.

Medium

• 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.