Preparing High School Teachers to Integrate AI Methods into STEM Classrooms

Source publication / research team or educational organization described in paper

United States

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

• In-school (K-12)

Workshop / professional learning activity

Not specified in extracted text

reserved. education because AI curricula that are relevant and engag- ing to K-12 students are only now being developed, few teachers are prepared to offer AI education, and there are no National educational st; mer camps. A program part- ner serving the Southwest region of the US recruited 10 participants (Cohort 1) for the first summer workshop held online in June 2021. Another 9 participants (Cohort 2) were recruited thr; ants (Cohort 1) for the first summer workshop held online in June 2021. Another 9 participants (Cohort 2) were recruited through a program partner in the Northeast region of the US for the second summer workshop he

ML concepts / supervised 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.

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.
• In this experience report, we describe an Artificial Intelli- gence (AI) Methods in Data Science (DS) curriculum and professional development (PD) program designed to prepare high school teachers with AI content knowledge and an un- derstanding of the ethical

• 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.

ML concepts / supervised learning

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design, Teacher professional development, Ethics / responsible AI education.
• AI capability focus: ML concepts / supervised learning.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• 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.

• 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.
• 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 curriculum consists of 5-day units on Data Analytics, Decision trees, Machine Learning, Neural Net- works, and Transfer learning that follow a scaffolded learn- ing progression consisting of introductions to concepts grounded in everyday experiences, hands-on activities, in- teractive web-based

• The curriculum consists of 5-day units on Data Analytics, Decision trees, Machine Learning, Neural Net- works, and Transfer learning that follow a scaffolded learn- ing progression consisting of introductions to concepts grounded in everyday experiences, hands-on activities, in- teractive web-based
• We share findings on teacher outcomes from the implementation of two one-week PD workshops during the summer of 2021 and share sugges- tions for improvements provided by teachers.

In this experience report, we describe an Artificial Intelli- gence (AI) Methods in Data Science (DS) curriculum and professional development (PD) program designed to prepare high school teachers with AI content knowledge and an un- derstanding of the ethical issues posed by bias in AI to sup- port their integration of AI methods into existing STEM classrooms. The curriculum consists of 5-day units on Data Analytics, Decision trees, Machine Learning, Neural Net- works, and Transfer learning that follow a scaffolded learn- ing progression consisting of introductions to concepts grounded in everyday experiences, hands-on activities, in- teractive web-based tools, and inspecting and modifying the code used to build, train and test AI models within Google Colab notebooks.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• 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
• Ethics and responsible use
• Teacher readiness
• Curriculum / course design
• Teacher professional development
• Ethics / responsible AI education
• ML concepts / supervised learning

• Completed

9-12

In-school (K-12)

ML concepts / supervised learning

Hands-on / experiential learning

Medium

Medium

Preparing High School Teachers to Integrate AI Methods into STEM Classrooms

• Irene Lee
• Beatriz Perret

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

2022

10.1609/aaai.v36i11.21557

https://ojs.aaai.org/index.php/AAAI/article/view/21557

https://ojs.aaai.org/index.php/AAAI/article/view/21557/21306

102_Preparing High School Teachers to Integrate AI Methods into STEM Classrooms.pdf

9

In this experience report, we describe an Artificial Intelli- gence (AI) Methods in Data Science (DS) curriculum and professional development (PD) program designed to prepare high school teachers with AI content knowledge and an un- derstanding of the ethical issues posed by bias in AI to sup- port their integration of AI methods into existing STEM classrooms. The curriculum consists of 5-day units on Data Analytics, Decision trees, Machine Learning, Neural Net- works, and Transfer learning that follow a scaffolded learn- ing progression consisting of introductions to concepts grounded in everyday experiences, hands-on activities, in- teractive web-based tools, and inspecting and modifying the code used to build, train and test AI models within Google Colab notebooks. The participants in the PD program were secondary school teachers from the Southwest and North- east regions of the United States who represented a variety of STEM disciplines: Biology, Chemistry, Physics, Engi- neering, and Mathematics. We share findings on teacher outcomes from the implementation of two one-week PD workshops during the summer of 2021 and share sugges- tions for improvements provided by teachers. We conclude with a discussion of affordances and challenges encountered in preparing teachers to integrate AI education into discipli- nary classrooms.

• In-school (K-12)

• 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

• 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.