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Computational Thinking with Computer Vision: Developing AI Competency in an Introductory Computer Science Course
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Describes and evaluates a first-year introductory CS course that teaches computational thinking through computer vision projects, reading discussions on AI ethics, and a personal final project, using pre/post surveys with 12 students.
Problem
Introductory CS courses provide limited opportunities to develop AI competency and critical thinking about AI's societal and ethical implications, and most prior AI-education research targets advanced courses.
Approach
Uses computer vision as an application context in a CS1-equivalent course to teach computational thinking alongside critical thinking (reading assignments, debates, a personal final project), evaluated via pre/post surveys on belonging, self-efficacy, and AI ethics awareness.
Key results
- Two self-efficacy items improved significantly: understanding AI build procedures (+1.42, p<0.05) and interpreting model outputs (+1.59, p<0.05).
- Sense of belonging item 'I feel like an outsider in AI' (reverse coded) improved significantly (+0.39, p<0.05).
- AI ethics awareness items did not change significantly, which the authors attribute to already high initial awareness.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Take the computer vision course | Introductory CS students · Sense of belonging in AI, item 3 (reverse coded) | mean response: +0.3939 | Helped |
| Take the computer vision course | Introductory CS students · Self-efficacy in AI, confidence understanding procedures and main steps of building an AI tool | mean response: +1.4242 | Helped |
| Take the computer vision course | Introductory CS students · Self-efficacy in AI, confidence interpreting AI model outputs | mean response: +1.5909 | Helped |
| Take the computer vision course | Introductory CS students · AI ethics awareness | survey response | No clear effect |
Surprising findings
- AI ethics awareness did not significantly improve despite the ethics-focused readings and discussions, possibly due to high baseline awareness.
Limitations
- Very small sample (n=12) from a single small undergraduate institution limits generalization.
- Only first offering evaluated; placement test for advanced students still under exploration.
- Trade-off between CS and AI depth; students found switching topics hard to follow.
Experimental setup
Datasets: pre- and post-course Likert surveys (n=12) student comments on Perusall reading annotations
Metrics: mean change in Likert ratings Wilcoxon signed-rank test p-value thematic analysis of student comments
Builds on: context-based learning open-ended programming assignments Perusall social annotation
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