How New Developers Approach Augmented Reality Development Using Simplified Creation Tools: An Observational Study
Bibliographic record
Abstract
Software developers new to creating Augmented Reality (AR) experiences often gravitate towards simplified development environments, such as 3D game engines. While popular game engines such as Unity and Unreal have evolved to offer extensive support and functionalities for AR creation, many developers still find it difficult to realize their immersive development projects. We ran an observational study with 12 software developers to assess how they approach the initial AR creation processes using a simplified development framework, the information resources they seek, and how their learning experience compares to the more mainstream 2D development. We observed that developers often started by looking for code examples rather than breaking down complex problems, leading to challenges in visualizing the AR experience. They encountered vocabulary issues and found trial-and-error methods ineffective due to a lack of familiarity with 3D environments, physics, and motion. These observations highlight the distinct needs of emerging AR developers and suggest that conventional code reuse strategies in mainstream development may be less effective in AR. We discuss the importance of developing more intuitive training and learning methods to foster diversity in developing interactive systems and support self-taught learners.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".