The Discoverability of Intelligent Personal Assistant Skills Through Voice Interfaces
Bibliographic record
Abstract
A wide discrepancy exists between the range of available Intelligent Personal Assistant (IPA) skills and the range of skills users engage with regularly. One reason for this is the issue of skill discoverability. From the literature, we understand that there are factors that can potentially enhance discoverability, such as context of use. The literature also signals that current discoverability strategies, which leverage such factors, are being challenged by users’ privacy concerns, and rapid skill growth. Mindful of these challenges, we explore the ways users naturally group IPA skills as a possible springboard for discoverability. Through an open card sort, we find that users’ clustering processes are guided by a combination of five categories of factors—Thematic, Procedural, Cross-System, Environmental, and Recipient Factors. Our findings suggest that IPAs should support all of these categories. We discuss how conversational interfaces are poised to support this diversity of pathways for discoverability.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".