Going Over the Wall: Supporting Critical Artificial Intelligence Literacy Using Narrative Design Fiction
Bibliographic record
Abstract
Artificial intelligence (AI) has become increasingly embedded in every aspect of our lives and educators are beginning to consider how to teach with and about it. Most AI curricula distinctly focus on developing digital or physical technical skills such as coding, robotics, and programming, while only sometimes critically considering the social and ethical dimensions of AI. This may lead to a future disparity between critical thinking and technical competency in AI literacy programming. This qualitative case study research focuses on how a week-long virtual camp used narrative design fiction in graphic novel format as a framework for camp activities and discussions for students in grades 6-8, to facilitate conversations related to the social and ethical implications of AI use. Results suggest that participants gained deeper and more complex opinions on AI and human-technology relationships via critical conversations facilitated through the narrative design fiction. Recommendations for future work on speculative futures, reflection, and narrative design fiction are presented.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".