Exploring User Satisfaction with AI-enabled Voice- Activated Smart Phone Assistants
Bibliographic record
Abstract
Voice-Activated Smart Phone Assistants (VASPA) have, in the recent past, assumed the identity of the most common forms of Artificial Intelligence (AI). VASPA, as an application in the education settings both at pedagogy and andragogy levels, continues to find acceptance. This research focuses on Students' confirmation of expectation and satisfaction with the performance of the VASPAs. An empirical study at one of the leading Canadian Universities will be carried out. Data will be collected through the help of a post-use questionnaire. The questionnaire will be administered after a lecture scenario of 3-5 minutes about a Student's topic of interest. Students will be allowed to select any of their preferred VASPAs, out of the following three: Siri, Google Assistant, and Bixby. Empirical findings will be presented, along with the implications for both the teachers and students. We will also discuss the possible future research directions for VASPAs.
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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 | 0.003 | 0.019 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".