Embrace your incompetence! Designing appropriate CUI communication through an ecological approach
June 21, 2022 Β· Declared Dead Β· π International Conference on Conversational User Interfaces
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Sophie Becker, Philip Doyle, Justin Edwards
arXiv ID
2206.10273
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
International Conference on Conversational User Interfaces
Last Checked
4 months ago
Abstract
People form impressions of their dialogue partners, be they other people or machines, based on cues drawn from their communicative style. Recent work has suggested that the gulf between people's expectations and the reality of CUI interaction widens when these impressions are misaligned with the actual capabilities of conversational user interfaces (CUIs). This has led some to rally against a perceived overriding concern for naturalness, calling instead for more representative, or appropriate communicative cues. Indeed, some have argued for a move away from naturalness as a goal for CUI design and communication. We contend that naturalness need not be abandoned, if we instead aim for ecologically grounded design. We also suggest a way this might be achieved and call on CUI designers to embrace incompetence! By letting CUIs express uncertainty and embarrassment through ecologically valid and appropriate cues that are ubiquitous in human communication - CUI designers can achieve more appropriate communication without turning away from naturalness entirely.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted