Small Group Interactions with Voice-User Interfaces

Ostrowski, Anastasia K.; Zygouras, Vasiliki; Park, Hae Won; Breazeal, Cynthia

doi:10.1145/3434073.3444655

Cited by 10 publications

(7 citation statements)

References 68 publications

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“…For example, two papers [172,173] indicated recruitment of participants with "high technology acceptance" but without details on the type of technology. Other studies focused on participants' familiarity with broad subsets of technological products, such as video games [171,174], VR systems [105,175], smartphones [176,177], and social media [178,179]. Some studies covered details about familiarity with specific types of technology, such as the Rviz visualization widget [180], maps in the game Unreal Tournament 2004 [181], and Alternative Augmented Communication (AAC) products [128].…”

Section: Domain Expertisementioning

confidence: 99%

“…Others [205,206] used classifications, such as experts and novice users. Still others [177,207] simply reported the presence or absence or previous interactions between participants and robots. With this in mind, we identified 69 studies reporting that at least some participants were already familiar with robots and 97 where at least some were not.…”

Section: Domain Expertisementioning

confidence: 99%

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Not Only WEIRD but “Uncanny”? A Systematic Review of Diversity in Human–Robot Interaction Research

Seaborn

Barbareschi

Chandra

2023

Int J of Soc Robotics

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Critical voices within and beyond the scientific community have pointed to a grave matter of concern regarding who is included in research and who is not. Subsequent investigations have revealed an extensive form of sampling bias across a broad range of disciplines that conduct human subjects research called “WEIRD”: Western, Educated, Industrial, Rich, and Democratic. Recent work has indicated that this pattern exists within human–computer interaction (HCI) research, as well. How then does human–robot interaction (HRI) fare? And could there be other patterns of sampling bias at play, perhaps those especially relevant to this field of study? We conducted a systematic review of the premier ACM/IEEE International Conference on Human-Robot Interaction (2006–2022) to discover whether and how WEIRD HRI research is. Importantly, we expanded our purview to other factors of representation highlighted by critical work on inclusion and intersectionality as potentially underreported, overlooked, and even marginalized factors of human diversity. Findings from 827 studies across 749 papers confirm that participants in HRI research also tend to be drawn from WEIRD populations. Moreover, we find evidence of limited, obscured, and possible misrepresentation in participant sampling and reporting along key axes of diversity: sex and gender, race and ethnicity, age, sexuality and family configuration, disability, body type, ideology, and domain expertise. We discuss methodological and ethical implications for recruitment, analysis, and reporting, as well as the significance for HRI as a base of knowledge.

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Section: Domain Expertisementioning

confidence: 99%

Section: Domain Expertisementioning

confidence: 99%

Not Only WEIRD but “Uncanny”? A Systematic Review of Diversity in Human–Robot Interaction Research

Seaborn

Barbareschi

Chandra

2023

Int J of Soc Robotics

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“…On the same issue, Wang et al (2022) identify a five-step evaluation framework for co-design, that is (i) considering evaluation from the beginning of the co-design process; (ii) co-defining key performance indicators and the evaluation criteria; (iii) selecting methods and techniques for the assessment; (iv) critically interpreting the results and (v) gaining additional feedback and increasing the validity. While, Ostrowski, Breazeal & Park (2021) use Zimmerman et al ’s (2007) four lenses for RTD evaluation (i.e. “process,” “invention,” “relevance” and “extensibility”) to evaluate co-design in an RTD process.…”

Section: Introductionmentioning

confidence: 99%

“…It is the case where “design partners do more than just inform the direction for the future, they are active participants in the next design.” However, in some cases (e.g. Taylor 2017; Ostrowski et al 2021), even if the usage of co-design is an integral part of an RTD approach, the process itself is presented as “through design” without any mentioned change concerning the mental model for managing the collaborative process in the RTD process. Similar relationships and challenges emerge from those works (e.g.…”

Section: Introductionmentioning

confidence: 99%

Research through co-design

Busciantella-Ricci,

Scataglini

2024

Des. Sci.

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Research Through Design (RTD) needs to reconsider the meaning of “designing” in the research process of “through design.” We propose Research Through Co-design (RTC) as a new application of Control System Theory (CST) that includes a research problem assigned to a co-design process in RTD. It embeds the participatory paradigm through collaborative design practice and makes the research a collaborative process for learning from all the participants. To sustain the RTC theory, we present a cognitive model of RTC. It is a “model for” – rather than a “model of” – describing how the co-design, as a neural network process, works through its nodes’ collaboration to find co-designed solutions and the research answer. Diversity increases as non-experts and non-designers with different backgrounds participate. This is valuable for the RTC learning system. The discussions highlight the possibility of considering (i) the RTC model as useful for describing a robust RTD process through CST; (ii) RTC as a cognitive model for explaining the value of co-design in research processes; and (iii) RTC as a strategy for applying the participative paradigm in formal research. Finally, new insights and implications are highlighted, including using RTC as a predictive tool through artificial intelligence.

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“…A RA can be more expressive than a VA thanks to its expressive motions [4] or facial expressions, such as Haru [6], who can express its mood by changing the positions/shapes of its eyes. Studies showed that RA use is more enjoyable [7] than VAs and that they are often preferred due to their social embodiment [8]. Another difference between HRHI robots and RAs is that the latter provide direct interaction between the user and the robot, and no third party is included in the loop, even if the whole family can use it, contrary to HRHI ones.…”

Section: Introductionmentioning

confidence: 99%

Yōkobo: A Robot to Strengthen Links Amongst Users with Non-Verbal Behaviours

et al. 2022

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Yōkobo is a robject; it was designed using the principle of slow technology and it aims to strengthen the bond between members (e.g., a couple). It greets people at the entrance and mirrors their interactions and the environment around them. It was constructed by applying the notions of a human–robot–human interaction. Created by joint work between designers and engineers, the form factor (semi-abstract) and the behaviours (nonverbal) were iteratively formed from the early stage of the design process. Integrated into the smart home, Yōkobo uses expressive motion as a communication medium. Yōkobo was tested in our office to evaluate its technical robustness and motion perception ahead of future long-term experiments with the target population. The results show that Yōkobo can sustain long-term interaction and serve as a welcoming partner.

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Small Group Interactions with Voice-User Interfaces

Cited by 10 publications

References 68 publications

Not Only WEIRD but “Uncanny”? A Systematic Review of Diversity in Human–Robot Interaction Research

Not Only WEIRD but “Uncanny”? A Systematic Review of Diversity in Human–Robot Interaction Research

Research through co-design

Yōkobo: A Robot to Strengthen Links Amongst Users with Non-Verbal Behaviours

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