Designing Voice Interfaces: Back to the (Curriculum) Basics
Bibliographic record
Abstract
Voice user interfaces (VUIs) are rapidly increasing in popularity in the consumer space. This leads to a concurrent explosion of available applications for such devices, with many industries rushing to offer voice interactions for their products. This pressure is then transferred to interface designers; however, a large majority of designers have been only trained to handle the usability challenges specific to Graphical User Interfaces (GUIs). Since VUIs differ significantly in design and usability from GUIs, we investigate in this paper the extent to which current educational resources prepare designers to handle the specific challenges of VUI design. For this, we conducted a preliminary scoping scan and syllabi meta review of HCI curricula at more than twenty top international HCI departments, revealing that the current offering of VUI design training within HCI education is rather limited. Based on this, we advocate for the updating of HCI curricula to incorporate VUI design, and for the development of VUI-specific pedagogical artifacts to be included in new curricula.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".