Thriving in the Age of AI: A Model Curriculum for Developing Competencies in Artificial Intelligence for K-12
Bibliographic record
Abstract
Ontario Tech University’s Engineering Outreach developed the course “Thriving in the Age of Artificial Intelligence (AI)” to provide high school students with a foundational understanding of AI, addressing the increasing relevance of these technologies in everyday life, education, and the workforce. This research paper outlines the course curriculum and delivery methods and evaluates the course’s impact on students and educators. The curriculum consists of six core modules that align with the Ontario Curriculum, focusing on equipping youth with essential AI knowledge and exploring its societal implications. The course is delivered through online platforms, workshops, and direct classroom integration, designed to expand access to AI education, particularly for students and educators with limited previous exposure. Using qualitative data from student feedback and quantitative metrics on course interaction, this paper analyzes the course’s effectiveness in improving AI literacy and its influence on educators’ instructional practices. Findings indicate that students who completed the course demonstrated an improved grasp of key AI concepts, preparing them for future career opportunities in an AI-driven world. Additionally, the course has provided educators with strategies for integrating AI into their teaching practices, enhancing their confidence and instructional competencies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".