An Evaluation of Inclusive Augmented Reality Prototyping Techniques for Non Technical Users
Bibliographic record
Abstract
This project analyzes the current state of low-fidelity augmented reality prototyping.The project examines recent works in low-fidelity prototyping for augmented reality, as well as conducting a cognitive walkthrough for a higher fidelity, yet no-coding-required augmented reality authoring tool.LensStudio is a popular digital augmented reality prototyping tool.All recent low-fidelity augmented reality prototyping studies were examined to compare methods and materials.This study compares the use of a custom prototype kit to another popular low-fidelity prototyping method, sketching, to see if one method produced more positive results for novice augmented reality users.These results are then compared with a group of more experienced users performing the same task.Results showed that though some materials in the proposed kit provided inspiration to novices, the materials also constrained them.This study proposes a workflow that involves novice AR users being presented with some overview information to properly ground themselves in what AR can do, and then to use their scenario to inform what materials should be part of their kit.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".