AI Literacy Through a Project-Based Learning Course
Bibliographic record
Abstract
The rapid emergence of generative Artificial Intelligence (AI) tools has sparked both excitement and concern in education. This paper reports on a case study of British Columbia Institute of Technology’s COMP 2800 – Term 2 Computer Systems Technology Project course, which was redesigned to foster AI literacy through a five-week project-based learning approach. In Spring 2023, approximately 230 first-year computing students (forming 60 teams across two campuses) built web applications addressing an "AI for Good" challenge. The course structure combined Agile sprints, reflective retrospectives, and open-ended problem solving with an expectation to integrate generative AI (e.g., ChatGPT, GitHub Copilot, and Midjourney). We described the course design, the diverse ways students leveraged AI tools in their projects, and a standout project case study. Qualitative and quantitative data from team retrospectives were analyzed to assess outcomes in estimation skills, teamwork, ethical awareness, and prompt engineering proficiency. The results indicate that project-based integration of AI can enhance students’ functional AI literacy and prompt critical thinking about AI’s role. We discuss implications for computing education, including pedagogical strategies for incorporating AI into curriculum and preparing students for responsible AI-augmented software development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".