PONI: A Personalized Onboarding Interface for Getting Inspiration and Learning About AR/VR Creation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
New creators of augmented reality (AR) and virtual reality (VR) applications often face a steep learning curve during the onboarding stage of creation and struggle in identifying suitable learning materials that are appropriate for their skillsets. To support the initial learning needs of new AR/VR creators from different backgrounds, we designed and implemented a novel personalized onboarding interface (PONI) that allows users to locate relevant projects based on their programming and 3D modeling skills, development goals, and any constraints, such as time or budget. Our usability evaluation (n=16) showed that most creators found PONI to be intuitive, useful, and saw its potential to be used as a knowledge hub for inspiration and self-directed exploratory learning. We discuss ways in which the personalization could be further enhanced and how the potential of PONI could be explored to improve onboarding in contexts beyond AR/VR development.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it