AI and Parallelism in CS1: Experiences and Analysis

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

• Higher education

Course implementation or course design

Not specified in extracted text

Not specified in extracted text

ML concepts / supervised learning

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

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.
• This work considers the use of AI and parallelism as a context for learning typical programming concepts in an introductory programming course (CS1).

• 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.
• AI capability focus: ML concepts / supervised learning.

• Apply standard AAB safeguards: privacy, transparency, human oversight, and documentation of limitations.

• 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.

• Game-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

• Case classified under: Published empirical study.
• Pedagogical pattern: Game-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 course includes exercises in decision trees, a novel game called Find the Gnomes to intro- duce supervised learning, the construction and application of a vectorized neural network unit class, and obtaining speedup in training through parallelism.

• The course includes exercises in decision trees, a novel game called Find the Gnomes to intro- duce supervised learning, the construction and application of a vectorized neural network unit class, and obtaining speedup in training through parallelism.
• The exercises are designed to teach students typical introductory programming concepts while also providing a preview and motivating example of advanced CS topics.

This work considers the use of AI and parallelism as a context for learning typical programming concepts in an introductory programming course (CS1). The course includes exercises in decision trees, a novel game called Find the Gnomes to intro- duce supervised learning, the construction and application of a vectorized neural network unit class, and obtaining speedup in training through parallelism.

• Apply standard AAB safeguards: privacy, transparency, human oversight, and documentation of limitations.

• 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
• Curriculum / course design
• ML concepts / supervised learning

• Completed

Higher education

Higher education

ML concepts / supervised learning

Game-based learning

Low to Medium

Medium

AI and Parallelism in CS1: Experiences and Analysis

• Steven Bogaerts

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

2023

10.1609/aaai.v37i13.26876

https://ojs.aaai.org/index.php/AAAI/article/view/26876

https://ojs.aaai.org/index.php/AAAI/article/view/26876/26648

067_AI and Parallelism in CS1_ Experiences and Analysis.pdf

9

This work considers the use of AI and parallelism as a context for learning typical programming concepts in an introductory programming course (CS1). The course includes exercises in decision trees, a novel game called Find the Gnomes to intro- duce supervised learning, the construction and application of a vectorized neural network unit class, and obtaining speedup in training through parallelism. The exercises are designed to teach students typical introductory programming concepts while also providing a preview and motivating example of advanced CS topics. Students’ understanding and motivation are considered through a detailed analysis of pre- and post- survey data gathered in several sections of the course each taught by one of four instructors across five semesters.

• Higher education

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

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.