"I WANT": Agency and Accessibility in the Age of AI
Bibliographic record
Abstract
"I WANT access to public buildings and technologies"; "I WANT all stairs to have railings"; "I WANT there to be a talking pedestrian sign"; "I WANT curbs to be more noticeable"; "I WANT technology that is dedicated to the blind". Young, vision-impaired learners from across the world, participating in our team’s human-centered research and participatory design initiatives, express an impassioned desire for agency and inclusive space making. Utilizing these statements as a foundational element of the participatory design process, our work continues to explore the intersection of AI and inclusive space-making, the methods employed through human-centered research and computational techniques such as machine learning and app development, and the potential contributions of these interventions to a more accessible future. This paper presents a two-part investigation into the role of advanced technological interventions and participatory design in shaping the future of architecture and design. Part 1 explores the outcomes of AI assistive device research centered on the voices of future professionals. This phase involved interviews and focus group discussions with blind and visually impaired individuals, designers, and computer scientists in an ongoing human subject research, leading to the creation of an AI-driven navigation app. Part 2 anticipates the deployment of working prototypes derived from these participatory design processes during [Affiliation Placeholder]'s annual Blind Design Workshop, in which more than a dozen young people with vision-impairment participate each spring. Its itinerary includes analog exercises in drawing and model-making (using material samples and wax sticks on Braille graph paper), guided tours of multi-sensory learning spaces across [Location Placeholder], accessible training in the production of 3D-prints and embossed drawings, and mentorship from practicing design professionals of the vision-impaired community, culminating in a final presentation and group critique of accessible design proposals. The workshop is a unique career exploration experience in architecture for individuals with vision impairment, designed to empower them with the understanding that they can have agency in the space-making process by giving them a voice and teaching them to architect their ambitions for the future. The synergy of AI and architecture presents profound opportunities to propel young, vision-impaired individuals from passive observers to active participants in crafting inclusive environments. Our paper discusses how innovative approaches to research and learning can seed future generations with the goal of harnessing AI for social impact in design and substantiating their role as the vanguards of a more accessible world. The outcomes of this study hold the potential to shape pedagogical strategies and industry standards, contributing to a profound reimagining of inclusive design education and practice.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".