AI Education and Design Fiction
Bibliographic record
Abstract
As artificial intelligence (AI) increasingly permeates various sectors of society, it becomes crucial to develop educational approaches that prepare students for the concepts, applications, consequences and ethical considerations of AI. This dissertation investigates how current AI education strategies can be enhanced to better prepare K-12 students for the challenges associated with understanding and navigating AI’s impact on society. Specifically, it examines the potential of Design Fiction Pedagogy (DFP) in advancing AI education for addressing a gap in existing pedagogical methods. The research employs both theoretical studies and an empirical case study. The theoretical component develops a model of DFP, which integrates speculative design with narrative learning principles. The empirical investigation involves a case study of two separate week-long AI camps conducted in Ontario, Canada, where DFP was applied to engage students in exploring possible AI futures. Key findings reveal that design fiction, as a pedagogical method, may offer benefits for AI education. The DFP model has the potential to enhance AI education through its flexible adaptation in various settings. By integrating speculative design and narrative techniques, DFP makes abstract AI concepts and applications more accessible and relevant, fostering a deeper understanding among students. Additionally, it encourages students to consider the ethical implications and future impacts of AI technologies, promoting both ethical reflection and critical thinking. This approach may improve AI educational outcomes and equip students with the mindset and skills needed to navigate the applications and consequences of AI with a future-oriented perspective. By highlighting the potential of design fiction to bridge theoretical and practical aspects of AI, this research offers insights for educators and researchers aiming to prepare students for the challenges of a technology-driven future. DFP’s integration into AI education represents a step towards more engaging and effective teaching strategies. Further exploration and refinement of DFP is needed, to better understand its role in shaping educational practices and enhancing students’ readiness for an increasingly complex technological landscape.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".