Exploring Artificial Intelligence in English Language Arts with StoryQ

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)
• Professional / adult learning

Curriculum design or implementation

Not specified in extracted text

chine learning workflow using StoryQ, a web- based GUI environment for Grades 6-12 learners. In this module, students work with unstructured text data and learn to train, test, and improve text classification mo; chine learning workflow using StoryQ, a web- based GUI environment for Grades 6-12 learners. The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23) 15999 AI Concepts Addressed in the Module This

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

• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

6-8, 9-12, Adult / workforce

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.
• Exploring Artificial Intelligence (AI) in English Language Arts (ELA) with StoryQ is a 10-hour curriculum module designed for high school ELA classes.

• 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.

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

Language context discussed in source publication

• Co-creator

• Primary interaction pattern inferred from publication: Curriculum / course design, Learning tool / resource design.
• AI capability focus: Generative AI, Computer vision / image classification, NLP / text classification, ML concepts / supervised learning.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.
• 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.

• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• 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 module introduces students to fundamental AI concepts and essential machine learning workflow using StoryQ, a web- based GUI environment for Grades 6-12 learners.

• The module introduces students to fundamental AI concepts and essential machine learning workflow using StoryQ, a web- based GUI environment for Grades 6-12 learners.
• In this module, students work with unstructured text data and learn to train, test, and improve text classification models such as intent recognition, clickbait filter, and sentiment analysis.
• As they interact with machine-learning language models deeply, students also gain a nuanced understanding of language and how to wield it, not just as a data structure, but as a tool in our human-human encounters as well.
• The current version contains eight lessons, all delivered through a full-featured online learning and teaching platform.

Exploring Artificial Intelligence (AI) in English Language Arts (ELA) with StoryQ is a 10-hour curriculum module designed for high school ELA classes. The module introduces students to fundamental AI concepts and essential machine learning workflow using StoryQ, a web- based GUI environment for Grades 6-12 learners.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.
• 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
• Engagement / motivation
• Curriculum / course design
• Learning tool / resource design
• Generative AI
• Computer vision / image classification
• NLP / text classification
• ML concepts / supervised learning

• Completed

6-8, 9-12, Adult / workforce

In-school (K-12), Professional / adult learning

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

Instructional / curriculum-based learning

Medium

Medium

Exploring Artificial Intelligence in English Language Arts with StoryQ

• Jie Chao
• Rebecca Ellis
• Shiyan Jiang
• Carolyn Rosé
• William Finzer
• Cansu Tatar
• James Fiacco
• Kenia Wiedemann

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

2023

10.1609/aaai.v37i13.26899

https://ojs.aaai.org/index.php/AAAI/article/view/26899

https://ojs.aaai.org/index.php/AAAI/article/view/26899/26671

090_Exploring AI in English Language Arts with StoryQ.pdf

5

Exploring Artificial Intelligence (AI) in English Language Arts (ELA) with StoryQ is a 10-hour curriculum module designed for high school ELA classes. The module introduces students to fundamental AI concepts and essential machine learning workflow using StoryQ, a web- based GUI environment for Grades 6-12 learners. In this module, students work with unstructured text data and learn to train, test, and improve text classification models such as intent recognition, clickbait filter, and sentiment analysis. As they interact with machine-learning language models deeply, students also gain a nuanced understanding of language and how to wield it, not just as a data structure, but as a tool in our human-human encounters as well. The current version contains eight lessons, all delivered through a full-featured online learning and teaching platform. Computers and Internet access are required to implement the module. The module was piloted in an ELA class in the Spring of 2022, and the student learning outcomes were positive. The module is currently undergoing revision and will be further tested and improved in Fall 2022. Background1 Artificial Intelligence (AI) is transforming numerous industries and generating enormous wealth. However, the advancement in AI is reshaping the workforce, impacting people whose jobs can be replaced or redefined by AI systems. The wealth generated by AI advancement is unevenly distributed across different demographic groups, exacerbating existing inequities in society. Inequalities arising from current AI development are partially rooted in the unequal access to AI educational opportunities. K-12 is the critical stage for young people to develop foundational knowledge and interest in AI-related careers. At minimum, students need to understand that the current approach to AI development is based on machine learning (ML) from data and that data needs to be structured in ways such that machines can learn meaningful patterns (Touretzky et al., 2019). Ultimately, students should understand the roles and responsibilities of AI developers and potential pathways for their own participation in AI development.

• In-school (K-12)
• Professional / adult learning

• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• 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.