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Record W4225149910 · doi:10.1145/3491102.3517599

Expressive Auditory Gestures in a Voice-Based Pedagogical Agent

2022· article· en· W4225149910 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCHI Conference on Human Factors in Computing Systems · 2022
Typearticle
Languageen
FieldComputer Science
TopicSpeech and dialogue systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGestureComputer scienceSpeech recognitionArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we explore how expressive auditory gestures added to the speech of a pedagogical agent influence the human-agent relationship and learning outcomes. In a between-subjects experiment, 41 participants assumed the role of a tutor to teach a voice-based agent. The agent used either: expressive interjections (e.g.,“yay”, “hmm”, “oh”), brief expressive musical executions, or no auditory gestures at all (control condition), throughout the interaction. Overall, the results indicate that both gestures can positively affect the interaction, but in particular, interjections can significantly increase feelings of emotional rapport with the agent and enhance motivation in learners. The implications of our findings are discussed as our work adds to the understanding of conversational agent design and can be useful for education as well as other domains in which dialogue systems are used.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.174
GPT teacher head0.348
Teacher spread0.174 · 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