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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".