Using Online Videos as the Basis for Developing Design Guidelines: A Case Study of AR-Based Assembly Instructions
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
Design guidelines serve as an important conceptual tool to guide designers of interactive applications with well-established principles and heuristics. Consulting domain experts is a common way to develop guidelines. However, experts are often not easily accessible, and their time can be expensive. This problem poses challenges in developing comprehensive and practical guidelines. We propose a new guideline development method that uses online public videos as the basis for capturing diverse patterns of design goals and interaction primitives. In a case study focusing on AR-based assembly instructions, we apply our novel Identify-Rationalize pipeline, which distills design patterns from videos featuring AR-based assembly instructions (N=146) into a set of guidelines that cover a wide range of design considerations. The evaluation conducted with 16 AR designers indicated that the pipeline is useful for generating comprehensive guidelines. We conclude by discussing the transferability and practicality of our method.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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