A Study of Students’ Learning of Computing through an LP-Based Integrated Curriculum for Middle Schools

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)

Curriculum design or implementation

Not specified in extracted text

Not specified in extracted text

AI literacy / AI concepts

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

6-8

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.
• There has been a consensus on integrating computing into the teaching and learning of STEM (Science, Technology, En- gineering and Math) subjects in K-12 (Kindergarten to 12th grade in the US education system).

• 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.

AI literacy / AI concepts

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design.
• AI capability focus: AI literacy / AI concepts.

• 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 empirical study.
• 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.
• However, rigorous study on the impact of an integrated curriculum on students’ learn- ing in computing and/or the STEM subject(s) is still rare.

• However, rigorous study on the impact of an integrated curriculum on students’ learn- ing in computing and/or the STEM subject(s) is still rare.
• In this paper, we report our research on how well an inte- grated curriculum helps middle school students learn comput- ing (abstraction and programming) through the microgenetic analysis methods.

There has been a consensus on integrating computing into the teaching and learning of STEM (Science, Technology, En- gineering and Math) subjects in K-12 (Kindergarten to 12th grade in the US education system). However, rigorous study on the impact of an integrated curriculum on students’ learn- ing in computing and/or the STEM subject(s) is still rare.

• 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
• Curriculum / course design
• AI literacy / AI concepts

• Completed

6-8

In-school (K-12)

AI literacy / AI concepts

Instructional / curriculum-based learning

Low to Medium

Medium

A Study of Students’ Learning of Computing through an LP-Based Integrated Curriculum for Middle Schools

• Joshua Archer
• Rory Eckel
• Joshua Hawkins
• Jianlan Wang
• Darrel Musslewhite
• Yuanlin Zhang

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

2023

10.1609/aaai.v37i13.26875

https://ojs.aaai.org/index.php/AAAI/article/view/26875

https://ojs.aaai.org/index.php/AAAI/article/view/26875/26647

066_A Study of Students#U2019 Learning of Computing through an LP-Based Integrated Curriculum for Middle Schools.pdf

8

There has been a consensus on integrating computing into the teaching and learning of STEM (Science, Technology, En- gineering and Math) subjects in K-12 (Kindergarten to 12th grade in the US education system). However, rigorous study on the impact of an integrated curriculum on students’ learn- ing in computing and/or the STEM subject(s) is still rare. In this paper, we report our research on how well an inte- grated curriculum helps middle school students learn comput- ing (abstraction and programming) through the microgenetic analysis methods.

• 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.

High

• 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.