Exploring AR hand augmentations as error feedback mechanisms for enhancing gesture-based tutorials
Bibliographic record
Abstract
Self-guided tutorials from videos help users learn new skills and complete tasks with varying complexity, from repairing a gadget to learning how to play an instrument. However, users may struggle to interpret 3D movements and gestures from 2D representations due to different viewpoints, occlusions, and depth perception. Augmented Reality (AR) can alleviate this challenge by enabling users to view complex instructions in their 3D space. However, most approaches only provide feedback if a live expert is present and do not consider self-guided tutorials. Our work explores virtual hand augmentations as automatic feedback mechanisms to enhance self-guided, gesture-based AR tutorials. We evaluated different error feedback designs and hand placement strategies on speed, accuracy and preference in a user study with 18 participants. Specifically, we investigate two visual feedback styles — color feedback, which changes the color of the hands’ joints to signal pose correctness, and shape feedback, which exaggerates fingers length to guide correction — as well as two placement strategies: superimposed, where the feedback hand overlaps the user’s own, and adjacent, where it appears beside the user’s hand. Results show significantly faster replication time when users are provided with color or baseline no explicit feedback, when compared to shape manipulation feedback. Furthermore, despite users’ preferences for adjacent placement for the feedback representation, superimposed placement significantly reduces replication time. We found no effects on accuracy for short-time recall, suggesting that while these factors may influence task efficiency, they may not strongly affect overall task proficiency.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".