Voice-Enabled Intelligent Virtual Agents for People With Amnesia: Systematic Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Older adults often have increasing memory problems (amnesia), and approximately 50 million people worldwide have dementia. This syndrome gradually affects a patient over a period of 10-20 years. Intelligent virtual agents may support people with amnesia. OBJECTIVE This study aims to identify state-of-the-art experimental studies with virtual agents on a screen capable of verbal dialogues with a target group of older adults with amnesia. METHODS We conducted a systematic search of PubMed, SCOPUS, Microsoft Academic, Google Scholar, Web of Science, and CrossRef on virtual agent and amnesia on papers that describe such experiments. Search criteria were (Virtual Agent OR Virtual Assistant OR Virtual Human OR Conversational Agent OR Virtual Coach OR Chatbot) AND (Amnesia OR Dementia OR Alzheimer OR Mild Cognitive Impairment). Risk of bias was evaluated using the QualSyst tool (University of Alberta), which scores 14 study quality items. Eligible studies are reported in a table including country, study design type, target sample size, controls, study aims, experiment population, intervention details, results, and an image of the agent. RESULTS A total of 8 studies was included in this meta-analysis. The average number of participants in the studies was 20 (SD 12). The verbal interactions were generally short. The usability was generally reported to be positive. The human utterance was seen in 7 (88%) out of 8 studies based on short words or phrases that were predefined in the agent’s speech recognition algorithm. The average study quality score was 0.69 (SD 0.08) on a scale of 0 to 1. CONCLUSIONS The number of experimental studies on talking about virtual agents that support people with memory problems is still small. The details on the verbal interaction are limited, which makes it difficult to assess the quality of the interaction and the possible effects of confounding parameters. In addition, the derivation of the aggregated data was difficult. Further research with extended and prolonged dialogues is required.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".