A Socially Relevant Focused AI Curriculum Designed for Female High 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)
• Informal learning

Curriculum design or implementation

35 hours of contact time with participants; 4 hours a day over the course of 10 weekdays

stages. First, we found relevant and open source AI and ML materials to teach K-12 students. Understanding that the curriculum is intended to fit a 35- to 40-hour summer camp or course-based 9-week schedule, we c

NLP / text classification, Assessment / tutoring analytics

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

9-12

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.
• Historically, female students have shown low interest in the field of computer science.

• 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, Assessment / tutoring analytics

Language context discussed in source publication

• Tutor

• Primary interaction pattern inferred from publication: Curriculum / course design, Teacher professional development, Assessment support, Outreach / informal learning.
• AI capability focus: NLP / text classification, Assessment / tutoring analytics.

• 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.

• Hands-on / experiential learning, Tutoring / feedback-supported learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• 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, Tutoring / feedback-supported 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.
• Our new summer camp curriculum intro- duces the topics of artificial intelligence (AI), machine learn- ing (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cut- ting edge technologies.

• Our new summer camp curriculum intro- duces the topics of artificial intelligence (AI), machine learn- ing (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cut- ting edge technologies.
• Sum- mer camp teachers were prepared in a week-long pedagogy and peer-teaching centered professional development pro- gram where they concurrently learned and practiced teaching the curriculum to one another.

Historically, female students have shown low interest in the field of computer science. Previous computer science curric- ula have failed to address the lack of female-centered com- puter science activities, such as socially relevant and real- life applications.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• Survey
• 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
• Engagement / motivation
• Teacher readiness
• Assessment / feedback quality
• Curriculum / course design
• Teacher professional development
• Assessment support
• Outreach / informal learning

• Completed

9-12

In-school (K-12), Informal learning

NLP / text classification, Assessment / tutoring analytics

Hands-on / experiential learning, Tutoring / feedback-supported learning

Low to Medium

Medium

A Socially Relevant Focused AI Curriculum Designed for Female High School Students

• Lauren Alvarez
• Isabella Gransbury
• Veronica Cateté
• Tiffany Barnes, Ákos Ledéczi
• Shuchi Grover

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

2022

10.1609/aaai.v36i11.21546

https://ojs.aaai.org/index.php/AAAI/article/view/21546

https://ojs.aaai.org/index.php/AAAI/article/view/21546/21295

099_A Socially Relevant Focused AI Curriculum Designed for Female High School Students.pdf

8

Historically, female students have shown low interest in the field of computer science. Previous computer science curric- ula have failed to address the lack of female-centered com- puter science activities, such as socially relevant and real- life applications. Our new summer camp curriculum intro- duces the topics of artificial intelligence (AI), machine learn- ing (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cut- ting edge technologies. Topics range from social media bots, sentiment of natural language in different media, and the role of AI in criminal justice, and focus on programming activities in the NetsBlox and Python programming languages. Sum- mer camp teachers were prepared in a week-long pedagogy and peer-teaching centered professional development pro- gram where they concurrently learned and practiced teaching the curriculum to one another. Then, pairs of teachers led stu- dents in learning through hands-on AI and ML activities in a half-day, two-week summer camp. In this paper, we discuss the curriculum development and implementation, as well as survey feedback from both teachers and students.

• In-school (K-12)
• Informal learning

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• 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

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.