On the Use of Indigenous Games in Teaching Software Engineering and Artificial Intelligence
Bibliographic record
Abstract
Concepts in Software Engineering and Artificial Intelligence are often taught by using games as motivation. The structured nature of a game makes it easier to understand the decisions that an agent must make, and it gives us a platform for defining suitable algorithms. In this paper, we point out that this well-known approach can be effectively used in the classroom to support diversity and inclusion. We present two lesson plans for teaching programming concepts using traditional Indigenous games for motivation. These lessons are just as effective as they would be using games from some other cultural context. However, using Indigenous games has several advantages. First, the use of these games can create a more inclusive classroom environment, where we are pushing away from a model where cultural artefacts from the colonial culture implicitly have primacy. Moreover, drawing games from a variety of cultural contexts helps to reduce any bias towards students from any particular cultural tradition. Finally, using these games provides an opportunity to add a little bit of indigenous history and culture into the classroom in a natural way.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".