An empirically grounded sociotechnical perspective on designing virtual agents for older adults
Bibliographic record
Abstract
Autonomous, intelligent virtual agents (IVAs) are increasingly used commercially in essential information spaces such as healthcare. Existing IVA research has focused on microscale interaction patterns, for example those related to the usability of artificial intelligence systems. However, the sociotechnical patterns of users’ information practices and their relationship with the design and adoption of IVAs have been largely understudied, especially when it comes to older adults, who stand to benefit greatly from IVAs. Yet, exposing such patterns may more meaningfully relate sociotechnical considerations to users’ perceptions and attitudes toward the adoption of emerging technologies such as IVAs. We explore here the feasibility of information models in informing our understanding of how older adults may use and perceive an IVA. To do this, we relate the insights and findings from a case study of health information IVAs to the six stages of the information search process model (ISP). By doing this, we uncover sociotechnical issues pertinent to each stage of the ISP which help to better contextualize (older) users’ interaction with intelligent interfaces such as IVAs. Through this, we argue for the potential of information models to inform the design of interactive user interfaces from a sociotechnical approach.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 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 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".