The Role of Partial Automation in Increasing the Accessibility of Digital Games
Bibliographic record
Abstract
Digital games are designed to be controlled using hardware devices such as gamepads, keyboards, and cameras. Some device inputs may be inaccessible to players with motor impairments, rendering them unable to play. Games and devices can be adapted to enable play, but for some players these adaptations may not go far enough. Games may require inputs that some players cannot provide with any device. To address this problem, we introduce partial automation, an accessibility technique that delegates control of inaccessible game inputs to an AI partner. Partial automation complements and builds on other approaches to improving games' accessibility, including universal design, player balancing, and interface adaptation. We have demonstrated partial automation in two games for the rehabilitation of spinal cord injury. Six study participants with vastly different motor abilities were able to play both games. Participants liked the increased personalization that partial automation affords, although some participants were confused by aspects of the AI's behaviour.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".