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Record W4414015804 · doi:10.11159/mhci25.121

Beyond “Alexa, good morning”: prerequisites for a voice assistant that truly understands older adults with empathy

2025· article· en· W4414015804 on OpenAlexvenueno aff
Juliana Camargo, Telmo Silva, Jorge Abreu

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2025
Typearticle
Languageen
FieldComputer Science
TopicAI in Service Interactions
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsEmpathyComputer scienceMorningHuman–computer interactionSpeech recognitionPsychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Older adults show interest in technological resourcesa perception we were able to confirm through field tests conducted with 20 individuals aged between 60 and 89 in Portugal.Participants were challenged to use the virtual assistant Alexa to contact family members and perform other daily activities.A total of 6,301 interactions were recorded during the study, across different categories.The analysis of these interactions, combined with post-test interviews and the results of a loneliness scale, revealed gaps that hindered the interaction between Alexa and the participants.As a result, we were able to identify key requirements for the development of a more empathetic and age-appropriate voice assistant.In summary, the findings point to the potential of such technology to reduce loneliness and facilitate communication with family members, while also highlighting limitations related to language and command comprehension.Based on the results, we propose a set of design requirements for a virtual assistant tailored to the needs of older adults.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.213
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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