Reimagining AI Futures. Solarpunk Narratives in Design Education
Bibliographic record
Abstract
The narrative imaginary surrounding Artificial Intelligence (AI) has been predominantly shaped by science fiction cinema and television, with dystopian scenarios prevailing. This dominance poses a significant challenge by constraining designers’ creative capacities to envision alternatives beyond the dystopian genre. It reinforces fear-based representations, fostering a polarizing perspective of being either for or against AI, rather than embracing a range of diverse viewpoints. Such narrative fundamentalism jeopardizes the foundational values of freedom, equity, and democracy central to design education, narrowing the scope for fostering inclusive and complex understandings of AI. To counteract these limitations, this research introduces the Solarpunk movement as an alternative framework. Solar punks hopeful and ecologically sustainable vision provides a compelling counter- narrative, enabling designers to explore optimistic and actionable futures. This study employs a qualitative methodology, integrating a critical literature review with case study analysis, focusing on the Solarpunk themes evident in the film The Wild Robot. Through this analysis, we identify recurring narrative elements and pragmatic aspects of Solarpunk that hold the potential for creating alternative design futures. The findings underscore the importance of empowering design scholars and practitioners to engage with AI integration constructively. Designers are positioned as key facilitators of transition, mediating between emerging technologies and societal needs. By adopting Solar punks values, they can actively shape collaborative and sustainable futures. This research highlights the pivotal role of design educators in equipping future designers with the tools and perspectives necessary to craft inclusive narratives, thereby fostering a more democratic and innovative design practice for an AI-integrated society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".