Let’s Talk About CUIs: Putting Conversational User Interface Design Into Practice
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
As CUIs become more prevalent in both academic research and the commercial market, it becomes more essential to design usable and adoptable CUIs. Though research on the usability and design of CUIs has been growing greatly over the past decade, we see that many usability issues are still prevalent in current conversational voice interfaces, from issues in feedback and visibility, to learnability, to error correction, and more. These issues still exist in the most current conversational interfaces in the commercial market, like the Google Assistant, Amazon Alexa, and Siri. The aim of this workshop therefore is to bring both academics and industry practitioners together to bridge the gaps of knowledge in regards to the tools, practices, and methods used in the design of CUIs. This workshop will bring together both the research performed by academics in the field, and the practical experience and needs from industry practitioners, in order to have deeper discussions about the resources that require more research and development, in order to build better and more usable CUIs.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it