Fostering critical thinkers and future designers: Design fiction pedagogy in AI education
Bibliographic record
Abstract
The pervasive impact of artificial intelligence (AI) on society underscores the critical need for comprehensive AI education, particularly for young students. This study investigates how design fiction pedagogy (DFP) may enhance K-12 AI education by fostering understanding of AI and encouraging critical thinking about its social impact. Grounded in constructivist and constructionist theories, DFP integrates speculative design and narrative learning to provide an interdisciplinary pedagogical approach. The DFP model, which consists of seven pedagogical steps—researching a problem, designing a prototype, creating a future context, building a narrative, sharing with stakeholders, reflecting on ethical considerations, and evaluating and redesigning—was implemented through two separate week-long AI education camps in Ontario, Canada. Using a qualitative case study approach, we examined how DFP supports upper elementary students in grasping AI concepts and AI’s social impact. The findings indicate that, by integrating technical principles, ethical considerations, and futuristic thinking about human-AI interactions, DFP helps students develop a deeper understanding of AI while fostering creativity, critical thinking, and futuristic thinking—skills essential for the responsible development and use of AI. Consequently, DFP emerges as a promising approach for an AI education that integrates technical knowledge with ethical considerations.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".