Accessibility in virtual reality: A multimodal user experience framework for considering hardware, embodied, and spatial access
Bibliographic record
Abstract
Virtual reality (VR) systems and other emerging technologies have transformed how professional writers and teams interact with information and navigate digital environments (Caravella, Shivener, & Narayanamoorthy, 2022; Saker & Frith, 2020; Tham, 2024). In Meta's Horizons Workrooms , users interact with shared virtual whiteboards, chalk, and spatial audio (Shivener & Tham, in press). In BigScreenVR , collaborative meetings include shared computer screens, 3D audio, and facial gestures (Shivener & Caravella, 2025). These platforms allow for the integration of visual, auditory, and spatial elements in innovative ways, pushing the boundaries of digital writing and collaboration across various points of the writing process. Drawing on VR and UX theories, our pedagogies, and recent qualitative studies of writing in VR (Shivener & Tham, in press; Shivener & Caravella, 2025), this piece proposes three considerations that UX writing teachers must contend with before and as they integrate VR into a classroom: hardware, embodied, and spatial access. UX and multimodal composition teachers are well positioned to engage VR but must anticipate accessibility challenges that have complicated previous studies and pedagogies. In addition to the concerns themselves, we also outline potential example assignments and pedagogical methods for addressing these challenges. These practical guidelines inform lesson plans and experiences that are both engaging and equitable for a range of students, and provide a blueprint for teachers to include such technologies in UX classrooms in accessible ways.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".