Smit Apurva Desai: Hello, everyone. My name is Smit Desai, and today I'm going to be talking about the feasibility of using voice user interfaces for informal, self-regulated learning of health topics among younger and older adults.
Smit Apurva Desai: So, first of all, when we say voice user interfaces, we mean speech-based voice assistants like Amazon Alexa, or Google Assistant.
Smit Apurva Desai: Now these interfaces are very popular, and are used for a variety of purposes, including information retrieval, home automation, or even companionship, by a wide demographic of people, including older adults. Here, please let me take a moment to tell you a little story.
Smit Apurva Desai: Just before the pandemic began, my grandmother was planning to apply for a Canadian visa to see my aunt. She was very excited about this plan, but at the same time, she was extremely nervous about the highly dreaded visa interview
problem was, she had never spoken to anyone with a non-indian accent before. She thought she won't be able to understand what the visa officer was saying, and would fail the interview. I took this opportunity to design a simple voice agent that would ask her questions in Canadian accent, and help her learn and prepare for the interview.
Learning with this agent filled her with confidence, and she aced the interview.
Smit Apurva Desai: This made me realize the potential of using voice interfaces for informal learning, and this story became an inspiration and starting point for this project.
Similar to my grandmom, interest for learning using voice interfaces has been raised by the wider user community in research studies with VUIs, and the research community has also explored the possibility of deploying VUIs in pedagogical roles like a teacher, a tutor, and a coach.
But these studies have been limited to a fairly young user group. There is hardly any research understanding how older adults learn with voice interfaces, and how does it compare to their younger counterparts? But the question now is, what do people most popularly want to learn with VUIs
Smit Apurva Desai: Well, out of all information-seeking, behavior, seeking and learning health information is the most salient in studies with voice interfaces. Users routinely ask questions about specific health conditions like blood pressure or even day-to-day problems like a toothache.
We saw a rise in this behavior during the COVID-19 pandemic. However, commercially available voice interfaces are not ready to handle this. Interactions with the VUIs tend to be rife with conversational errors, and information breakdowns leading to poor interaction fluency. Moreover, the information the VUIs deliver
tend to not be credible or accurate. In a recent study, it was found that the information given by VUIs for health questions could have led to potential harm or death in about a quarter of scenarios.
So to solve this issue, we designed "Health Buddy." A conversational action developed in Dialogflow ES and deployed using a Google Home Mini smart speaker.
We designed this using Google's conversational design guidelines and also design guidelines sourced from recent VUI literature with older adults. Additionally, we identified three learning strategies in previous literature that were the most feasible to implement with VUIs and incorporated them in Health Buddy. These three learning strategies are monologue, conceptual diagramming, and scaffolding building.
Smit Apurva Desai: It is important to note here that the same content was delivered to the users with the different strategies. This picture is just a tiny snippet of a much longer interaction. In conceptual diagramming the health information was delivered, using bi-directional diagrams
Smit Apurva Desai: with concepts or topics and a line connecting them, denoting a relationship. In scaffolding building, the same information is provided using pumps and prompts, which are elicitations to the users to think deeper, quizzes to assess the learning, and motivation to provide positive reinforcements.
In the monologue strategy, the learning was provided using instructional-style monologues with no dialogical back and forth. The length of the interaction was roughly the same across each strategy.
Smit Apurva Desai: So Health Buddy was designed to have the functions of stop or resume, repeat the instructions in case the participants needed it.
Practice questions which they can access whenever they want, and navigation, to move from one fact to another, or even practice the questions again, if required. Health Buddy would deliver instructions, using three learning strategies which serve as modes of Health Buddy.
Smit Apurva Desai: So Health Buddy would deliver learning about two health topics in each mode. Health topics included regular sleep habits, and the relationship between heart health and cholesterol, and four more.
Smit Apurva Desai: We ask three research questions in this study. The first is how older and younger adults learn with Health Buddy. The second is how interaction fluency impacts the learning performance of older and younger adults, and how interaction fluency and learning performance influence the acceptance and long-term adoption of Health Buddy.
Smit Apurva Desai: To answer these questions, we recruited 51 adults, out of which 26 were younger adults, and 25 were older adults
Smit Apurva Desai: Due to the COVID-19 pandemic, we conducted this study online using Zoom. So first we had the participants fill a demographic questionnaire to understand their prior experiences with voice interfaces and their prior knowledge of the health instructions.
Smit Apurva Desai: Then we had the participants play around with the Google Home Mini to get comfortable, and once they indicated that they were ready, a learning session with Health Buddy was initiated. The participants interacted with Health Buddy, using 3 learning strategies with 2 topics delivered using each strategy.
After that an experimenter conducted a cued recall session by asking the participants what they remembered about each health topic.
Smit Apurva Desai: We use ideas recalled from the cued recall session as a proxy for learning performance.
Then the participants were asked to fill a technology acceptance questionnaire and a cognitive measure questionnaire to understand the technology acceptance and also measure the verbal ability and working memory.
Finally the participants were debriefed and compensated for their time. So what did we find. First of all, we found that both younger and older adults had equivalently good learning performance. In fact, our results were comparable with previous results from reading comprehension studies. However, we saw key differences in the learning strategies preferred by older and younger adults.
While younger adults took more advantage of scaffolding building and conceptual diagramming. Older adults performed significantly better using the monologue condition.
Then we found that the participants face a lot of conversational breakdowns due to a variety of reasons, including natural language processing error, feedback error, or system error. We operationalize all these categories of errors as interaction fluency. So higher interaction fluency would mean less errors.
Smit Apurva Desai: We found that interaction fluency was the biggest factor influencing learning performance. Both younger and older adults experienced similar levels of interaction fluency.
Moreover, less interaction fluency for both younger and older adults resulted in poor learning performance. Then we measured the effect of interaction fluency on technology acceptance and perceived long-term adoption of Health Buddy.
We found that interaction fluency did not impact the acceptance of younger adults, but older adults were adversely affected. Older adults who had higher interaction fluency were significantly likelier to accept and use Health Buddy.
Our results lead to some really interesting implications. Since we did not find any effect of prior VUI experience on learning performance, we say that learning with voice interfaces offers an easy option to impart learning with a relatively small learning curve.
Very interestingly, we saw that older adults learned as well as younger adults with VUIs
Smit Apurva Desai: There were no significant differences in learning performance. This is usually not the case. Younger adults tend to do a lot better on recall tasks than older adults, especially when a technology is involved. We did not see this.
It would be extremely interesting to further investigate this in longitudinal studies. And we also found that older and younger adults take advantages of different learning strategies
Smit Apurva Desai: There is a need of designing elder specific design strategies for learning. A lot of learning studies have involved a younger audience, so the strategies developed have been more suited to younger adults. This is because of the notion that is widely believed that older adults do not want to learn.
Smit Apurva Desai: We challenge this notion, and would like to see more researchers work in this area.
Smit Apurva Desai: In sum, we conclude that voice interfaces have great potential to be learning partners, especially for old adults. However, there are key differences in how older adults and younger adults learn, and we need to test out more strategies to see what would be more suitable for older adults.
Also, interaction fluency is a major barrier affecting learning and acceptance.
Smit Apurva Desai: These are the references that we use for this presentation, and I would like to thank all of you for listening. Thank you.