From Digital Storytelling to Design Fiction: Pedagogical Innovations in AI Education for K-12
Bibliographic record
Abstract
In today’s world, AI is not just for experts—it is woven into our daily lives. This makes it essential for students, even at the K–12 level, to develop the skills and understanding to engage meaningfully with AI. This paper explores two narrative-based pedagogical approaches—Digital Storytelling (DST) and Design Fiction Pedagogy (DFP)—for AI education in K–12 contexts. We first compare DST and DFP’s theoretical foundations, educational goals, tools, and affordances. While DST fosters student creativity and digital literacy through personal narrative, DFP extends this by integrating speculative design and ethical reflection. Drawing on conceptual analysis and comparative case studies—Ng et al.’s (2022) implementation of DST in Hong Kong and a 2024 DFP-based AI camp in Ontario, Canada—we examine how each approach supports student understanding of AI. Findings suggest that while DST engages learners in creative storytelling, DFP offers deeper conceptual engagement with future-oriented thinking, critical design, and ethical inquiry. This study lays the foundation for further research into DFP’s potential in AI education and its applicability to other STEAM disciplines, promoting innovation in teaching methodologies.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".