Computational Thinking with Computer Vision: Developing AI Competency in an Introductory Computer Science Course

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

29005 Semester Female White Asian Black Hispanic/Latinx Non-CS 1st yr

December term) as a 4-credit, 14-week course. Enrollment was capped at 25, and 12 students registered and com- pleted the course. The class met twice a week (Tuesdays and Thursdays) for 75-minute sessions, tota

Computer vision / image classification, Ethics / responsible AI

• 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.
• Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum.

• 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.

Computer vision / image classification, Ethics / responsible AI

Not specified in extracted text

• Evaluator

• Primary interaction pattern inferred from publication: Curriculum / course design, Ethics / responsible AI education.
• AI capability focus: Computer vision / image classification, Ethics / responsible AI.

• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.
• 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.

• Instructional / curriculum-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: 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, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge.

• However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge.
• This paper introduces an introductory CS course where students learn computational thinking through com- puter vision, a sub-field of AI, as an application context.
• Through ex- periential activities such as individual projects and reading discussions, our course seeks to balance technical learning and critical thinking goals.

Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge.

• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• 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.

• AI literacy
• Conceptual understanding
• Engagement / motivation
• Ethics and responsible use
• Curriculum / course design
• Ethics / responsible AI education
• Computer vision / image classification
• Ethics / responsible AI

• Completed

Higher education

Higher education

Computer vision / image classification, Ethics / responsible AI

Instructional / curriculum-based learning

Medium

Medium

Computational Thinking with Computer Vision: Developing AI Competency in an Introductory Computer Science Course

• Tahiya Chowdhury

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35170

https://ojs.aaai.org/index.php/AAAI/article/view/35170

https://ojs.aaai.org/index.php/AAAI/article/view/35170/37325

007_Computational Thinking with Computer Vision_ Developing AI Competency in an Introductory Computer Science Course.pdf

9

Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge. This paper introduces an introductory CS course where students learn computational thinking through com- puter vision, a sub-field of AI, as an application context. The course aims to achieve computational thinking outcomes alongside critical thinking outcomes that expose students to AI approaches and their societal implications. Through ex- periential activities such as individual projects and reading discussions, our course seeks to balance technical learning and critical thinking goals. Our evaluation, based on pre-and post-course surveys, shows an improved sense of belonging, self-efficacy, and AI ethics awareness among students. The results suggest that an AI-focused context can enhance partic- ipation and employability, student-selected projects support self-efficacy, and ethically grounded AI instruction can be ef- fective for interdisciplinary audiences. Students’ discussions on reading assignments demonstrated deep engagement with the complex challenges in today’s AI landscape. Finally, we share insights on scaling such courses for larger cohorts and improving the learning experience for introductory CS stu- dents.

• 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

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.