The Development of a Videogame With Accessibility Features Using Unreal Engine
Bibliographic record
Abstract
This paper documents the challenges and learning moments experienced in the development of an open world, action-adventure role-playing video game built with accessibility features using Unreal Engine. Given that at the outset, both Unreal Engine and the overall process of building a game were unfamiliar to me, this paper shares a unique perspective on the game development process through my experiences as a novice designer. Most significantly however, the paper's qualitative data derived from focus group research, which involved users living with various disabilities, offers insight into the usefulness of accessibility features built into video games. Specifically, this paper addresses the research question: "What key considerations should be taken into account when designing a video game for users with various disabilities?" The game, which features 12 different accessibility features that were constructed by accessing a number of learning resources and modifying assets to meet development needs, was demonstrated to a fivemember focus group of users with various disabilities. All respondents expressed the significance of accessibility features to their gaming experience by stating that they would prefer to play a lesser-known game with accessibility features than a popular release without such options. Their comments on the game and suggestions for accessibility enhancements to it are documented herein and are reflected in plans for its future development. Keywords:
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".