Digital Voice Assistants Research Articles

Children's trust in and learning from voice assistants.

As children increasingly interact with digital voice assistants, it is important to know whether they treat these devices as reliable information sources. Two studies investigated children's trust in and recall of statements made by a novel voice assistant and a human informant. In Study 1, children ages 4-5 (Mage = 5.05; 20 boys, 20 girls) and 7-8 (Mage = 7.98; 18 boys, 22 girls) from predominately White, upper middle-class families heard each informant respond to questions from multiple categories. With increasing age, children showed greater trust in the voice assistant for factual information and greater trust in the human for personal information about the experimenter identified as her friend. Endorsement of each informant's statements also predicted later recall. In Study 2, children ages 4-5 (Mage = 5.00; 20 boys, 20 girls) and 7-8 (Mage = 8.03; 19 boys, 21 girls) from predominately White, upper middle-class families chose whether to seek out information from a voice assistant or human informant. With increasing age, children showed an increasing preference to seek factual information from the voice assistant and an increasing preference to seek personal information from the human. Additionally, children's preferences were not related to attributions of epistemic capacities to each informant nor the presence of a voice assistant in children's homes. These results suggest that children's trust in voice assistants varies with age and depends on the type of information involved. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Using Alexa to Differentiate Instruction in the Special Education Classroom

Access to technologies and understanding the potential uses of technology to differentiate instruction have been a concern for the teachers and students in a local school district located in the southeastern United States. Despite the emergence of digital voice assistants (DVAs) as tools for instructions, teachers lack knowledge and strategies for using DVAs to differentiate instruction in their classrooms. The purpose of this basic qualitative study was to identify teacher knowledge and strategies employed among special education (SPED) teachers using DVAs to differentiate instruction in their classrooms. The concepts of Carol Tomlinson’s differentiation theory and Mishra and Koehler’s TPACK framework served as the foundation of this study. The research questions examined middle school SPED teachers’ perceptions of challenges using DVAs to differentiate instruction, resources, and strategies available to these teachers and their perceived knowledge of using DVAs to differentiate instruction. In this basic qualitative study, data were collected from 6 SPED teachers using semistructured interviews. The findings suggest that teachers had little to no perceived challenges when using DVAs to differentiate instructions. However, the overutilization of DVAs might rob students of their ability to think independently. This study offers several prospects for future research related to the topic and findings. Further research is needed at the elementary and high school levels that may include core content teachers.

Quality evaluation of digital voice assistants for the management of mental health conditions

<abstract><sec> <title>Background</title> <p>Digital voice assistants (DVAs) are gaining increasing popularity as a tool for accessing online mental health information. However, the quality of information provided by DVAs is not known. This study seeks to evaluate the quality of DVA responses to mental health-related queries in relation to six quality domains: comprehension ability, relevance, comprehensiveness, accuracy, understandability and reliability.</p> </sec><sec> <title>Materials and methods</title> <p>Four smartphone DVAs were evaluated: Apple Siri, Samsung Bixby, Google Assistant and Amazon Alexa. Sixty-six questions and answers on mental health conditions (depression, anxiety, obsessive-compulsive disorder (OCD) and bipolar disorder) were compiled from authoritative sources, clinical guidelines and public search trends. Three evaluators scored the DVAs from an in-house-developed evaluation rubric. Data were analyzed by using the Kruskal-Wallis and Wilcoxon rank sum tests.</p> </sec><sec> <title>Results</title> <p>Across all questions, Google Assistant scored the highest (78.9%), while Alexa scored the lowest (64.5%). Siri (83.9%), Bixby (87.7%) and Google Assistant (87.4%) scored the best for questions on depression, while Alexa (72.3%) scored the best for OCD questions. Bixby scored the lowest for questions on general mental health (0%) and OCD (0%) compared to all other DVAs. In terms of the quality domains, Google Assistant scored significantly higher for comprehension ability compared to Siri (100% versus 88.9%, p < 0.001) and Bixby (100% versus 94.5%, p < 0.001). Moreover, Google Assistant also scored significantly higher than Siri (100% versus 66.7%, p < 0.001) and Alexa (100% versus 75.0%, p < 0.001) in terms of relevance. In contrast, Alexa scored the worst in terms of accuracy (75.0%), reliability (58.3%) and comprehensiveness (22.2%) compared to all other DVAs.</p> </sec><sec> <title>Conclusion</title> <p>Overall, Google Assistant performed the best in terms of responding to the mental health-related queries, while Alexa performed the worst. While the comprehension abilities of the DVAs were good, the DVAs had differing performances in the other quality domains. The responses by DVAs should be supplemented with other information from authoritative sources, and users should seek the help and advice of a healthcare professional when managing their mental health conditions.</p> </sec></abstract>

Exploring value change

This article aims to explore the use of cross-impact balances (CIB) to identify scenarios of value change. The possibility of value change has received little attention in the literature on value-sensitive design (VSD). Examples of value change include the emergence of new values and changes in the relative importance of values. Value change could lead to a mismatch between values embedded in technology and the way they are currently considered in society. Such a mismatch could result in a lack of acceptability of technologies, increasing social tensions and injustices. However, methods to study value change in the VSD literature are rare. CIB is a scenario tool that can study systems characterized by feedback loops that are hard to describe mathematically. This is often the case when aiming to define values and their relationships. We demonstrate the use of CIB to identify scenarios of value change using two cases: digital voice assistants and gene drive organisms. Our findings show that CIB is helpful in building scenarios of value change, even in instances where the operationalization of values is complex. CIB also helps us to understand the mechanisms of value change and evaluate when such mechanisms occur. Finally, we find that CIB is particularly useful for social learning and explanatory modelling. CIB can therefore contribute to the design of value-sensitive technologies.

Anthropomorphizing Technology: A Conceptual Review of Anthropomorphism Research and How it Relates to Children’s Engagements with Digital Voice Assistants

‘Anthropomorphism’ is a popular term in the literature on human-technology engagements, in general, and child-technology engagements, in particular. But what does it really mean to ‘anthropomorphize’ something in today’s world? This conceptual review article, addressed to researchers interested in anthropomorphism and adjacent areas, reviews contemporary anthropomorphism research, and it offers a critical perspective on how anthropomorphism research relates to today’s children who grow up amid increasingly intelligent and omnipresent technologies, particularly digital voice assistants (e.g., Alexa, Google Assistant, Siri). First, the article reviews a comprehensive body of quantitative as well as qualitative anthropomorphism research and considers it within three different research perspectives: descriptive, normative and explanatory. Following a brief excursus on philosophical pragmatism, the article then discusses each research perspective from a pragmatistic viewpoint, with a special emphasis on child-technology and child-voice-assistant engagements, and it also challenges some popular notions in the literature. These notions include descriptive ‘as if’ parallels (e.g., child behaves ‘as if’ Alexa was a friend), or normative assumptions that human-human engagements are generally superior to human-technology engagements. Instead, the article reviews different examples from the literature suggesting the nature of anthropomorphism may change as humans’ experiential understandings of humanness change, and this may particularly apply to today’s children as their social cognition develops in interaction with technological entities which are increasingly characterized by unprecedented combinations of human and non-human qualities.

Children's mental, social, and moral attributions toward a familiar digital voice assistant

Children judge robots and computers based on social contingency, but little is known about how children treat semi-socially contingent devices like digital voice assistants. This study investigated children's attributions toward digital voice assistants. Children ages 6–10 (Mage = 8.46; 41 boys and 39 girls) from predominately white, upper-middle class families identified a familiar digital voice assistant and indicated whether it had mental, social, and moral attributes. For some questions, children also justified their responses. A majority of children assigned some mental or social attributes to digital voice assistants, but younger children were more likely than older children to view the digital voice assistant as having social and moral attributes. In their justifications, children who did not assign mental, social, or moral attributes to digital voice assistants more frequently noted the device's status as an artifact and its subsequent technological limitations. Children who did assign human-like attributes to a voice assistant more frequently cited the device's mental states and abilities. These findings suggest that younger children may be quicker to see new interactive technologies as social partners than older children and, consequently, to learn from and to share personal information with these devices.

Personalized privacy assistant for digital voice assistants: Case study on Amazon Alexa

The advancements in modern technologies permit the invention of various digital devices which are controlled and activated by people’s gestures, touch and even by one’s voice. Google Assistant, iPhone Siri, Amazon Alexa etc., are most popular voice enabled devices which have grabbed the attention of digital gadget users. Their usage definitely makes the life easier and comfortable. The other side of these smart enabled devices is incredible violation of the privacy. This happens due to their continuous listening to the user and data transmission over a public network to the third-party services. The work proposed in this paper attempts to overcome the existing privacy violation problem with the voice enabled devices. The main idea is to incorporate an intelligent privacy assistant that works based on the user preferences over their data.

NATURAL LANGUAGE PROCESSING AND SENTIMENT ANALYSIS

Natural Language Processing is one of the branches of Artificial Intelligence that has only recently entered the spotlight. Apple Siri, Amazon Alexa, and, more recently, Google Duplex are just a few of the most well-known instances of NLP in work, with the technology delivering outstanding human-machine interactions. By 2023, there are estimated to be eight billion digital voice assistants in use, owing to their popularity. With such a large-scale use, the data generated from these interactions would also be immense. This untapped goldmine of data can be used to further research and development of Natural Language Processing and can be used in many industries such as healthcare, technology, business etc. Sentiment analysis, with the help of Natural Language Processing can help industries process these huge datasets faster and efficiently and can be used, for example, in the healthcare industry to diagnose patients and develop diagnostic models for detecting chronic disease in its early stages. Web 2.0 has allowed massive user data to be generated which can be tapped to extract valuable information for various reasons that an individual, policy maker(s), organizations and Governments might need.

Automation, wellbeing and Digital Voice Assistants: Older people and Google devices

Digital Voice Assistants (DVAs) like Google Home provide automated news, media and other content directly into the home. In this article, we outline how Google Home’s content delivery can support the wellbeing and independence of older people. We argue that automated media provided by DVAs enrols older people in a dialectic relationship with the automated content and feminised conversation they deliver, uniquely performed within people’s own everyday life circumstances. We demonstrate this by drawing on ethnographic insights generated during a trial of smart home technologies with older Australian households who are ‘ageing in place’ in regional New South Wales. For most participants, the trial was their first encounter with DVAs and the modes of media and content delivery including for music, news, weather, trivia, jokes, facts and images. While DVAs bring new experiences via content, communication and companionship, they are also subverted, ignored or transformed as people improvise to make them ‘fit’ within their homes and lives. These dynamics underpin how DVAs, automated content delivery and user’s interactions can support people’s sense of wellness and their independent daily practices at home.

Who is more trustworthy, Alexa or mom?: Children’s selective trust in a digital age.

Children obtain much of their early knowledge informally through first-hand observations (e.g., children as little scientists). Though the “little scientist” approach has a strong history within cognitive development, a newer perspective suggests it is not only independent observation of the environment through which children learn but also by posing questions to trustworthy individuals around them. Now, in a digital age, children can inform their understanding of the world by asking questions not only of adults but also of digital agents who are able to scour the internet and provide quick responses to children’s questions. As a result of voice-search functionalities typically present in digital voice assistants (DVAs; e.g., Alexa), children can query the internet at an earlier age, sidestepping literacy-based prerequisite skills needed for traditional computer search engines. This review examines several child-level and technology-level factors that may influence children’s trust in DVAs relative to human informants. No work has combined these lines of inquiry in a way that has extended social (i.e., confidence, benevolence) and epistemic characteristics (i.e., accuracy, expertise) used in human-to-human interaction to digital voice assistant-to-human interaction. Results suggest DVS do not yet meet children’s expectations for a trustworthy informant, providing implications to improve DVA functionality for young users.

Will Baidu’s “All in AI” Strategy Bring It Back to the High-Speed Growth Train?

We examine whether Baidu’s “All in AI” strategy will return the company back to high growth. Baidu has suffered a slowdown in recent years, mainly due to the decline of search ads and revenue in a mobile era where Baidu is lagging its competitors, such as Alibaba, Tencent, or ByteDance. To make up, Baidu has been actively pursuing other revenue sources and decided on the “All in AI” strategy after some exploration. Baidu is aggressively investing in artificial intelligence (AI) technologies and striving to be a leader in AI. We examine five pillars under Baidu’s AI endeavors: the AI-driven mobile ecosystem, AI cloud, intelligent and autonomous driving, digital voice assistants, and AI chips. Although many of the AI initiatives, such as autonomous driving, are still in early stages with insignificant contribution to revenue, they are expected to become the growth engines for the future. In conclusion, our analysis shows Baidu’s AI efforts is promising yet the company will face many technological, financial, and political challenges in the next few years.

Internet-of-Things Devices in Support of the Development of Echoic Skills among Children with Autism Spectrum Disorder.

A significant therapeutic challenge for people with disabilities is the development of verbal and echoic skills. Digital voice assistants (DVAs), such as Amazon’s Alexa, provide networked intelligence to billions of Internet-of-Things devices and have the potential to offer opportunities to people, such as those diagnosed with autism spectrum disorder (ASD), to advance these necessary skills. Voice interfaces can enable children with ASD to practice such skills at home; however, it remains unclear whether DVAs can be as proficient as therapists in recognizing utterances by a developing speaker. We developed an Alexa-based skill called ASPECT to measure how well the DVA identified verbalization by autistic children. The participants, nine children diagnosed with ASD, each participated in 30 sessions focused on increasing vocalizations and echoic responses. Children interacted with ASPECT prompted by instructions from an Echo device. ASPECT was trained to recognize utterances and evaluate them as a therapist would—simultaneously, a therapist scored the child’s responses. The study identified no significant difference between how ASPECT and the therapists scored participants; this conclusion held even when subsetting participants by a pre-treatment echoic skill assessment score. This indicates considerable potential for providing a continuum of therapeutic opportunities and reinforcement outside of clinical settings.

Potential and Pitfalls of Digital Voice Assistants in Older Adults With and Without Intellectual Disabilities: Relevance of Participatory Design Elements and Ecologically Valid Field Studies.

While digital voice assistants (VAs) are increasingly becoming part of everyday life (Auxier, 2019), specific user groups that might benefit from such technology are often not considered in research. This applies to older adults (Stigall et al., 2019), a population with pronounced heterogeneity in terms of cognitive, sensory and functional competencies. Given the rapid expansion of digital tools, it is important to consider the use of VAs as a promising tool able to maintain and enhance social participation, autonomy, and leisure activities for a wide range of older adults. There is still a digital divide between younger and older generations (Anderson, 2019) and older adults might benefit to a smaller extent from the advantages of new technologies such as VAs. Older people with intellectual disabilities are at particular risk of digital exclusion (Ehlers et al., 2020) and digital technologies such as VAs have hardly been evaluated for this target group (e.g., Smith et al., 2020). In this opinion paper, we synthesize current research in the context of VAs for older adults. Building on this, we propose specific research designs to provide better insights into the adoption and use of VAs in advanced age.

Alexa, Affect, and the Algorithmic Imaginary

As millions of customers across the world invite digital voice assistants into their homes, the public debate has increasingly centered on security and privacy concerns connected to the use of the device. Drawing on Tania Bucher’s work at the intersection between technology and everyday experience, this article proposes an understanding of an algorithmic imaginary of Alexa-enabled devices as explicitly nonthreatening in its ordinariness, positive potential, and gendered presence. As a case study, this article uses commercials for Alexa-enabled devices as a starting point: Instead of foregrounding the functionality and thereby the algorithmic intelligence underlying the voice assistant, these commercials focus on an affective potential as a narrative strategy to address privacy and security concerns. By connecting everyday interactions with emotional and empowering narratives, the way Alexa is portrayed as an embodied object functions as a balance to the equally public and publicized understanding of digital voice assistants as threats.

Quality assessment of digital voice assistants on information provided in eating disorders and coexisting depression

Quality assessment of digital voice assistants on information provided in eating disorders and coexisting depression

\u201cAlexa, who am I?\u201d: Voice Assistants and Hermeneutic Lemniscate as the Technologically Mediated Sense-Making

In this paper, I argue that AI-powered voice assistants, just as all technologies, actively mediate our interpretative structures, including values. I show this by explaining the productive role of technologies in the way people make sense of themselves and those around them. More specifically, I rely on the hermeneutics of Gadamer and the material hermeneutics of Ihde to develop a hermeneutic lemniscate as a principle of technologically mediated sense-making. The lemniscate principle links people, technologies and the sociocultural world in the joint production of meaning and explicates the feedback channels between the three counterparts. When people make sense of technologies, they necessarily engage their moral histories to comprehend new technologies and fit them in daily practices. As such, the lemniscate principle offers a chance to explore the moral dynamics taking place during technological appropriation. Using digital voice assistants as an example, I show how these AI-guided devices mediate our moral inclinations, decisions and even our values, while in parallel suggesting how to use and design them in an informed and critical way.

Evaluation of COVID-19 Information Provided by Digital Voice Assistants

ABSTRACT Background: Digital voice assistants are widely used for health information seeking activities during the COVID-19 pandemic. Due to the rapidly changing nature of COVID-19 information, there is a need to evaluate COVID-related information provided by voice assistants, to ensure consumers’ needs are met and prevent misinformation. The objective of this study is to evaluate COVID-related information provided by the voice assistants in terms of relevance, accuracy, comprehensiveness, user-friendliness and reliability. Materials and Methods: The voice assistants evaluated were Amazon Alexa, Google Home, Google Assistant, Samsung Bixby, Apple Siri and Microsoft Cortana. Two evaluators posed COVID-19 questions to the voice assistants and evaluated responses based on relevance, accuracy, comprehensiveness, user-friendliness and reliability. Questions were obtained from the World Health Organization, governmental websites, forums and search trends. Data was analyzed using Pearson’s correlation, independent samples t-tests and Wilcoxon rank-sum tests. Results: Google Assistant and Siri performed the best across all evaluation parameters with mean scores of 84.0% and 80.6% respectively. Bixby performed the worst among the smartphone-based voice assistants (65.8%). On the other hand, Google Home performed the best among the non-smartphone voice assistants (60.7%), followed by Alexa (43.1%) and Cortana (13.3%). Smartphone-based voice assistants had higher mean scores than voice assistants on other platforms (76.8% versus 39.1%, p = 0.064). Google Assistant consistently scored better than Google Home for all the evaluation parameters. A decreasing score trend from Google Assistant, Siri, Bixby, Google Home, Alexa and Cortana was observed for majority of the evaluation criteria, except for accuracy, comprehensiveness and credibility. Conclusion: Google Assistant and Apple Siri were able to provide users with relevant, accurate, comprehensive, user-friendly, and reliable information regarding COVID-19. With the rapidly evolving information on this pandemic, users need to be discerning when obtaining COVID-19 information from voice assistants.

How Context Influences Cross-Device Task Acceptance in Crowd Work

Although crowd work is typically completed through desktop or laptop computers by workers at their home, literature has shown that crowdsourcing is feasible through a wide array of computing devices, including smartphones and digital voice assistants. An integrated crowdsourcing platform that operates across multiple devices could provide greater flexibility to workers, but there is little understanding of crowd workers’ perceptions on uptaking crowd tasks across multiple contexts through such devices. Using a crowdsourcing survey task, we investigate workers’ willingness to accept different types of crowd tasks presented on three device types in different scenarios of varying location, time and social context. Through analysis of over 25,000 responses received from 329 crowd workers on Amazon Mechanical Turk, we show that when tasks are presented on different devices, the task acceptance rate is 80.5% on personal computers, 77.3% on smartphones and 70.7% on digital voice assistants. Our results also show how different contextual factors such as location, social context and time influence workers decision to accept a task on a given device. Our findings provide important insights towards the development of effective task assignment mechanisms for cross-device crowd platforms.

Understanding consumers’ acceptance of automated technologies in service encounters: Drivers of digital voice assistants adoption

Customers increasingly orchestrate their everyday activities with the support of technology, with services increasingly adopting AI-based applications. Yet, research is still in its infancy and has been largely conceptual. Therefore, based on data collected from 238 young consumers, analyzed using PLS-SEM, this study focuses on users’ motivations to adopt intelligent digital voice assistants in service encounters. Findings show that functional, social and relational elements drive adoption, untangle crossover effects between them and reveal the moderating role of experience and need for human interaction. While empirically validating and extending the Service Robot Acceptance Model by Wirtz and colleagues, this study provides evidence that anthropomorphism is not universally positive and adds a new perspective regarding the underexplored role of customer-robot rapport building. The study contributes to a more holistic understanding of digital voice assistants’ adoption and provides managerial guidance on how to successfully implement such technologies.

Automatic Labeled Dialogue Generation for Nursing Record Systems.

The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that enable the machine to classify the purpose of the user utterance (intent) and extract pieces of valuable information present in the utterance (entity). One of the main obstacles when creating robust NLU is the lack of sufficient labeled data, which generally relies on human labeling. This process is cost-intensive and time-consuming, particularly in the high-level nursing care domain, which requires abstract knowledge. In this paper, we propose an automatic dialogue labeling framework of NLU tasks, specifically for nursing record systems. First, we apply data augmentation techniques to create a collection of variant sample utterances. The individual evaluation result strongly shows a stratification rate, with regard to both fluency and accuracy in utterances. We also investigate the possibility of applying deep generative models for our augmented dataset. The preliminary character-based model based on long short-term memory (LSTM) obtains an accuracy of 90% and generates various reasonable texts with BLEU scores of 0.76. Secondly, we introduce an idea for intent and entity labeling by using feature embeddings and semantic similarity-based clustering. We also empirically evaluate different embedding methods for learning good representations that are most suitable to use with our data and clustering tasks. Experimental results show that fastText embeddings produce strong performances both for intent labeling and on entity labeling, which achieves an accuracy level of 0.79 and 0.78 f1-scores and 0.67 and 0.61 silhouette scores, respectively.