Fostering critical thinkers and future designers: Design fiction pedagogy in AI education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it